{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook is used to conduct univariate and time series analysis of curatorial diversity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Load packages and dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "import numpy as np\n",
    "import matplotlib.colors as colors\n",
    "from scipy.stats import entropy\n",
    "from fuzzywuzzy import process\n",
    "import re\n",
    "import regex\n",
    "import requests\n",
    "import unicodedata\n",
    "from scipy.stats import kendalltau\n",
    "import ast\n",
    "import math\n",
    "from statsmodels.tsa.stattools import adfuller, kpss\n",
    "import squarify\n",
    "import seaborn as sb\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(\"bibliodata.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>text_title</th>\n",
       "      <th>year</th>\n",
       "      <th>reviewer</th>\n",
       "      <th>text_id</th>\n",
       "      <th>original_summary</th>\n",
       "      <th>book_title</th>\n",
       "      <th>author</th>\n",
       "      <th>book_publisher</th>\n",
       "      <th>...</th>\n",
       "      <th>book_year</th>\n",
       "      <th>authors_list</th>\n",
       "      <th>len title</th>\n",
       "      <th>reviewers_list</th>\n",
       "      <th>subject</th>\n",
       "      <th>subject_key</th>\n",
       "      <th>lcc_sort</th>\n",
       "      <th>lccn</th>\n",
       "      <th>id_goodreads</th>\n",
       "      <th>open_library</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>118</td>\n",
       "      <td>3687</td>\n",
       "      <td>Un libro-mortaja</td>\n",
       "      <td>2021</td>\n",
       "      <td>mateo navia hoyos</td>\n",
       "      <td>100148</td>\n",
       "      <td>1989. maría elvira samper. planeta, bogotá, 20...</td>\n",
       "      <td>1989</td>\n",
       "      <td>maria elvira samper</td>\n",
       "      <td>planeta</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>['maria elvira samper']</td>\n",
       "      <td>4.0</td>\n",
       "      <td>['mateo navia hoyos']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>/books/OL44260376M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1221</td>\n",
       "      <td>994</td>\n",
       "      <td>Dolor, violencia, horror</td>\n",
       "      <td>1995</td>\n",
       "      <td>dora cecilia ramirez</td>\n",
       "      <td>6028</td>\n",
       "      <td>\\n\\t\\t\\t\\t\\t&amp;ellas &amp;ellos, ángela maría pérez ...</td>\n",
       "      <td>ellas &amp; ellos: algunas caras femeninas</td>\n",
       "      <td>angela maria perez beltran</td>\n",
       "      <td>soluciones editoriales</td>\n",
       "      <td>...</td>\n",
       "      <td>1994</td>\n",
       "      <td>['angela maria perez beltran']</td>\n",
       "      <td>38.0</td>\n",
       "      <td>['dora cecilia ramirez']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1531</td>\n",
       "      <td>1304</td>\n",
       "      <td>\"Los verdaderos caballeros jamás trabajan\", de...</td>\n",
       "      <td>1998</td>\n",
       "      <td>luis h aristizabal</td>\n",
       "      <td>5629</td>\n",
       "      <td>\\n ...y se hizo la noche sobre ti. enrique dáv...</td>\n",
       "      <td>...y se hizo la noche sobre ti</td>\n",
       "      <td>enrique davila martinez</td>\n",
       "      <td>tercer mundo</td>\n",
       "      <td>...</td>\n",
       "      <td>1997</td>\n",
       "      <td>['enrique davila martinez']</td>\n",
       "      <td>30.0</td>\n",
       "      <td>['luis h aristizabal']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['97174872']</td>\n",
       "      <td>['5343112']</td>\n",
       "      <td>/books/OL774525M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0.1  Unnamed: 0  \\\n",
       "0           118        3687   \n",
       "1          1221         994   \n",
       "2          1531        1304   \n",
       "\n",
       "                                          text_title  year  \\\n",
       "0                                  Un libro-mortaja   2021   \n",
       "1                           Dolor, violencia, horror  1995   \n",
       "2  \"Los verdaderos caballeros jamás trabajan\", de...  1998   \n",
       "\n",
       "               reviewer  text_id  \\\n",
       "0     mateo navia hoyos   100148   \n",
       "1  dora cecilia ramirez     6028   \n",
       "2    luis h aristizabal     5629   \n",
       "\n",
       "                                    original_summary  \\\n",
       "0  1989. maría elvira samper. planeta, bogotá, 20...   \n",
       "1  \\n\\t\\t\\t\\t\\t&ellas &ellos, ángela maría pérez ...   \n",
       "2  \\n ...y se hizo la noche sobre ti. enrique dáv...   \n",
       "\n",
       "                               book_title                      author  \\\n",
       "0                                    1989         maria elvira samper   \n",
       "1  ellas & ellos: algunas caras femeninas  angela maria perez beltran   \n",
       "2          ...y se hizo la noche sobre ti     enrique davila martinez   \n",
       "\n",
       "           book_publisher  ... book_year                    authors_list  \\\n",
       "0                 planeta  ...      2019         ['maria elvira samper']   \n",
       "1  soluciones editoriales  ...      1994  ['angela maria perez beltran']   \n",
       "2            tercer mundo  ...      1997     ['enrique davila martinez']   \n",
       "\n",
       "  len title            reviewers_list subject subject_key lcc_sort  \\\n",
       "0       4.0     ['mateo navia hoyos']     NaN         NaN      NaN   \n",
       "1      38.0  ['dora cecilia ramirez']     NaN         NaN      NaN   \n",
       "2      30.0    ['luis h aristizabal']     NaN         NaN      NaN   \n",
       "\n",
       "           lccn id_goodreads        open_library  \n",
       "0           NaN          NaN  /books/OL44260376M  \n",
       "1           NaN          NaN                 NaN  \n",
       "2  ['97174872']  ['5343112']    /books/OL774525M  \n",
       "\n",
       "[3 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(n=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove spanish accents from authors, publishers, cities and reviewers names\n",
    "def clean_text(string):\n",
    "    try:\n",
    "        string = string.lower()\n",
    "        # Normalize and remove accents\n",
    "        string = ''.join(char for char in unicodedata.normalize('NFKD', string) if not unicodedata.combining(char))\n",
    "        # Remove all characters other than letters from a to z, ñ, commas, and whitespaces between words\n",
    "        cleaned_string = re.sub(r'[^a-zñ,\\s]+', '', string)\n",
    "    except:\n",
    "        cleaned_string = \"\"\n",
    "    return cleaned_string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location_publisher</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>oxford</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tolima</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>xalapa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>caracas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>pamplona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>popayan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>tokio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>faid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>barcelona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>berna</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>97 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   location_publisher\n",
       "0              oxford\n",
       "1              tolima\n",
       "2              xalapa\n",
       "3             caracas\n",
       "4            pamplona\n",
       "..                ...\n",
       "92            popayan\n",
       "93              tokio\n",
       "94               faid\n",
       "95          barcelona\n",
       "96              berna\n",
       "\n",
       "[97 rows x 1 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Clean the \"location_publisher\" column using the clean_text function\n",
    "df.rename(columns={\"book_city\": \"location_publisher\"}, inplace=True)\n",
    "df[\"location_publisher\"] = df[\"location_publisher\"].apply(clean_text)\n",
    "\n",
    "# Create a set to hold all unique publisher locations\n",
    "unique_locations = set()\n",
    "\n",
    "# Loop through each row in the DataFrame\n",
    "for index, row in df.iterrows():\n",
    "    locations = row['location_publisher'].split(\",\")\n",
    "    for location in locations:\n",
    "        location = location.strip()\n",
    "        if len(location) > 3:\n",
    "            unique_locations.add(location)\n",
    "\n",
    "# Create a new DataFrame with one row per unique publisher\n",
    "df_location_publishers = pd.DataFrame({'location_publisher': list(unique_locations)})\n",
    "\n",
    "# show publishers DataFrame\n",
    "(df_location_publishers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location_publisher</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>universidad externado de colombia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>villanueva de la serena espana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>teatro la candelaria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>santa rosa de osos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>palma de mallorca</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>chia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>cali</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>faid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>buga</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>leon</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>97 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   location_publisher\n",
       "29  universidad externado de colombia\n",
       "52     villanueva de la serena espana\n",
       "66               teatro la candelaria\n",
       "61                 santa rosa de osos\n",
       "53                  palma de mallorca\n",
       "..                                ...\n",
       "22                               chia\n",
       "46                               cali\n",
       "94                               faid\n",
       "59                               buga\n",
       "63                               leon\n",
       "\n",
       "[97 rows x 1 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort the index of df_location_publishers based on the length of publisher names in descending order\n",
    "s = df_location_publishers.location_publisher.str.len().sort_values(ascending=False).index\n",
    "\n",
    "# Reindex df_location_publishers\n",
    "df_location_publishers = df_location_publishers.reindex(s)\n",
    "\n",
    "# show reindexed publishers DataFrame\n",
    "(df_location_publishers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to find the best match for a publisher name in a list of names\n",
    "def find_best_match(publisher, publisher_list):\n",
    "    best_match, score = process.extractOne(publisher, publisher_list)\n",
    "    if score >= 90:  # Threshold for considering a match\n",
    "        return best_match\n",
    "    else:\n",
    "        return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>universidad externado de colombia</th>\n",
       "      <td>universidad externado de colombia</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>villanueva de la serena espana</th>\n",
       "      <td>espana</td>\n",
       "      <td>villanueva de la serena espana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>teatro la candelaria</th>\n",
       "      <td>teatro la candelaria</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>santa rosa de osos</th>\n",
       "      <td>santa rosa de osos</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>palma de mallorca</th>\n",
       "      <td>palma de mallorca</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chia</th>\n",
       "      <td>chia</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cali</th>\n",
       "      <td>cali</td>\n",
       "      <td>santiago de cali</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>faid</th>\n",
       "      <td>faid</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>buga</th>\n",
       "      <td>buga</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>leon</th>\n",
       "      <td>leon</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>97 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                   0  \\\n",
       "universidad externado de colombia  universidad externado de colombia   \n",
       "villanueva de la serena espana                                espana   \n",
       "teatro la candelaria                            teatro la candelaria   \n",
       "santa rosa de osos                                santa rosa de osos   \n",
       "palma de mallorca                                  palma de mallorca   \n",
       "...                                                              ...   \n",
       "chia                                                            chia   \n",
       "cali                                                            cali   \n",
       "faid                                                            faid   \n",
       "buga                                                            buga   \n",
       "leon                                                            leon   \n",
       "\n",
       "                                                                1  \n",
       "universidad externado de colombia                            None  \n",
       "villanueva de la serena espana     villanueva de la serena espana  \n",
       "teatro la candelaria                                         None  \n",
       "santa rosa de osos                                           None  \n",
       "palma de mallorca                                            None  \n",
       "...                                                           ...  \n",
       "chia                                                         None  \n",
       "cali                                             santiago de cali  \n",
       "faid                                                         None  \n",
       "buga                                                         None  \n",
       "leon                                                         None  \n",
       "\n",
       "[97 rows x 2 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a dictionary to keep track of the variations for each location of publisher\n",
    "location_variations = {}\n",
    "\n",
    "# Iterate over the unique publishers in the dataframe\n",
    "for location in df_location_publishers[\"location_publisher\"].unique():\n",
    "    variations = [location]  # Start with the original location\n",
    "    for other_location in df_location_publishers[\"location_publisher\"].unique():\n",
    "        if other_location != location:\n",
    "            match = find_best_match(location, [other_location])\n",
    "            \n",
    "            if match is not None and match not in variations:\n",
    "                variations.append(match)\n",
    "    variations.sort()\n",
    "    variations.sort(key=len)  # Sort the variations by length\n",
    "    location_variations[location] = variations\n",
    "\n",
    "# Create a DataFrame from the dictionary of location of publisher variations\n",
    "df_location_variations = pd.DataFrame.from_dict(location_variations, orient=\"index\")\n",
    "\n",
    "# Print the DataFrame with publisher variations\n",
    "(df_location_variations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location_publisher</th>\n",
       "      <th>location_variations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>universidad externado de colombia</td>\n",
       "      <td>universidad externado de colombia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>villanueva de la serena espana</td>\n",
       "      <td>espana,villanueva de la serena espana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>teatro la candelaria</td>\n",
       "      <td>teatro la candelaria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>santa rosa de osos</td>\n",
       "      <td>santa rosa de osos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>palma de mallorca</td>\n",
       "      <td>palma de mallorca</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>chia</td>\n",
       "      <td>chia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>cali</td>\n",
       "      <td>cali,santiago de cali</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>faid</td>\n",
       "      <td>faid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>buga</td>\n",
       "      <td>buga</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>leon</td>\n",
       "      <td>leon</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>97 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   location_publisher                    location_variations\n",
       "0   universidad externado de colombia      universidad externado de colombia\n",
       "1      villanueva de la serena espana  espana,villanueva de la serena espana\n",
       "2                teatro la candelaria                   teatro la candelaria\n",
       "3                  santa rosa de osos                     santa rosa de osos\n",
       "4                   palma de mallorca                      palma de mallorca\n",
       "..                                ...                                    ...\n",
       "92                               chia                                   chia\n",
       "93                               cali                  cali,santiago de cali\n",
       "94                               faid                                   faid\n",
       "95                               buga                                   buga\n",
       "96                               leon                                   leon\n",
       "\n",
       "[97 rows x 2 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter out missing data\n",
    "location_variations = df_location_variations.apply(lambda x: ','.join(x.astype(str)), axis=1)\n",
    "location_variations = location_variations.apply(lambda x: x.replace(\"None,\",\"\"))\n",
    "location_variations = location_variations.apply(lambda x: x.replace(\",None\",\"\"))\n",
    "\n",
    "# Convert the Series to a DataFrame and reset the index\n",
    "df_location_publishers = location_variations.to_frame().reset_index()\n",
    "\n",
    "# Rename the columns\n",
    "df_location_publishers = df_location_publishers.rename(columns={\"index\": \"location_publisher\", 0: \"location_variations\"})\n",
    "\n",
    "# Print the DataFrame with location of publisher variations\n",
    "(df_location_publishers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>text_title</th>\n",
       "      <th>year</th>\n",
       "      <th>reviewer</th>\n",
       "      <th>text_id</th>\n",
       "      <th>original_summary</th>\n",
       "      <th>book_title</th>\n",
       "      <th>author</th>\n",
       "      <th>book_publisher</th>\n",
       "      <th>...</th>\n",
       "      <th>book_year</th>\n",
       "      <th>authors_list</th>\n",
       "      <th>len title</th>\n",
       "      <th>reviewers_list</th>\n",
       "      <th>subject</th>\n",
       "      <th>subject_key</th>\n",
       "      <th>lcc_sort</th>\n",
       "      <th>lccn</th>\n",
       "      <th>id_goodreads</th>\n",
       "      <th>open_library</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>118</td>\n",
       "      <td>3687</td>\n",
       "      <td>Un libro-mortaja</td>\n",
       "      <td>2021</td>\n",
       "      <td>mateo navia hoyos</td>\n",
       "      <td>100148</td>\n",
       "      <td>1989. maría elvira samper. planeta, bogotá, 20...</td>\n",
       "      <td>1989</td>\n",
       "      <td>maria elvira samper</td>\n",
       "      <td>planeta</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>['maria elvira samper']</td>\n",
       "      <td>4.0</td>\n",
       "      <td>['mateo navia hoyos']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>/books/OL44260376M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1221</td>\n",
       "      <td>994</td>\n",
       "      <td>Dolor, violencia, horror</td>\n",
       "      <td>1995</td>\n",
       "      <td>dora cecilia ramirez</td>\n",
       "      <td>6028</td>\n",
       "      <td>\\n\\t\\t\\t\\t\\t&amp;ellas &amp;ellos, ángela maría pérez ...</td>\n",
       "      <td>ellas &amp; ellos: algunas caras femeninas</td>\n",
       "      <td>angela maria perez beltran</td>\n",
       "      <td>soluciones editoriales</td>\n",
       "      <td>...</td>\n",
       "      <td>1994</td>\n",
       "      <td>['angela maria perez beltran']</td>\n",
       "      <td>38.0</td>\n",
       "      <td>['dora cecilia ramirez']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1531</td>\n",
       "      <td>1304</td>\n",
       "      <td>\"Los verdaderos caballeros jamás trabajan\", de...</td>\n",
       "      <td>1998</td>\n",
       "      <td>luis h aristizabal</td>\n",
       "      <td>5629</td>\n",
       "      <td>\\n ...y se hizo la noche sobre ti. enrique dáv...</td>\n",
       "      <td>...y se hizo la noche sobre ti</td>\n",
       "      <td>enrique davila martinez</td>\n",
       "      <td>tercer mundo</td>\n",
       "      <td>...</td>\n",
       "      <td>1997</td>\n",
       "      <td>['enrique davila martinez']</td>\n",
       "      <td>30.0</td>\n",
       "      <td>['luis h aristizabal']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['97174872']</td>\n",
       "      <td>['5343112']</td>\n",
       "      <td>/books/OL774525M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2531</td>\n",
       "      <td>2344</td>\n",
       "      <td>¡Pffffff...!: una antología desinflada</td>\n",
       "      <td>2004</td>\n",
       "      <td>carlos soler</td>\n",
       "      <td>3987</td>\n",
       "      <td>\\n\\t\\t\\t\\t\\t¡aaaaaahhh...! doce relatos erótic...</td>\n",
       "      <td>¡aaaaaahhh...! doce relatos eróticos.</td>\n",
       "      <td>varios autores</td>\n",
       "      <td>planeta</td>\n",
       "      <td>...</td>\n",
       "      <td>2002</td>\n",
       "      <td>['varios autores ']</td>\n",
       "      <td>37.0</td>\n",
       "      <td>['carlos soler']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1286</td>\n",
       "      <td>1059</td>\n",
       "      <td>Un conjunto de anécdotas</td>\n",
       "      <td>2007</td>\n",
       "      <td>adolfo gonzalez henriquez</td>\n",
       "      <td>5955</td>\n",
       "      <td>\\n\\t\\t\\t\\t\\t¡baila, negro, baila! crónica de u...</td>\n",
       "      <td>¡baila, negro, baila! crónica de un salsero</td>\n",
       "      <td>rafael araujo gamez</td>\n",
       "      <td>universidad libre</td>\n",
       "      <td>...</td>\n",
       "      <td>2007</td>\n",
       "      <td>['rafael araujo gamez']</td>\n",
       "      <td>43.0</td>\n",
       "      <td>['adolfo gonzalez henriquez']</td>\n",
       "      <td>['Salsa (Dance)', 'Fiction']</td>\n",
       "      <td>['fiction', 'salsa_(dance)']</td>\n",
       "      <td>PQ-8180.10000000.R165 B3 2007</td>\n",
       "      <td>['2007470232']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>/books/OL16750076M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3376</th>\n",
       "      <td>3306</td>\n",
       "      <td>3494</td>\n",
       "      <td>El mundo como escritura de la fragmentación</td>\n",
       "      <td>2006</td>\n",
       "      <td>nelly rocio amaya mendez</td>\n",
       "      <td>94</td>\n",
       "      <td>\\n\\t\\t\\t\\t\\tzanahorias voladoras. antonio unga...</td>\n",
       "      <td>zanahorias voladoras</td>\n",
       "      <td>antonio ungar</td>\n",
       "      <td>alfaguara</td>\n",
       "      <td>...</td>\n",
       "      <td>2004</td>\n",
       "      <td>['antonio ungar']</td>\n",
       "      <td>20.0</td>\n",
       "      <td>['nelly rocio amaya mendez']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3377</th>\n",
       "      <td>769</td>\n",
       "      <td>542</td>\n",
       "      <td>El pavor habita los espejos</td>\n",
       "      <td>1990</td>\n",
       "      <td>rafael patiño goez</td>\n",
       "      <td>6645</td>\n",
       "      <td>\\n\\t\\t\\t\\t\\tzarpa y espejo. adolfo león córdob...</td>\n",
       "      <td>zarpa y espejo</td>\n",
       "      <td>adolfo leon cordoba narvaez</td>\n",
       "      <td>cuadernos de poesía ulrika</td>\n",
       "      <td>...</td>\n",
       "      <td>1990</td>\n",
       "      <td>['adolfo leon cordoba narvaez']</td>\n",
       "      <td>14.0</td>\n",
       "      <td>['rafael patiño goez']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3378</th>\n",
       "      <td>2234</td>\n",
       "      <td>2007</td>\n",
       "      <td>Entretelones de ciento cuarenta y cinco años d...</td>\n",
       "      <td>2015</td>\n",
       "      <td>martha enna rodriguez melo</td>\n",
       "      <td>4700</td>\n",
       "      <td>\\nZarzuela, opereta y ópera en Medellín, 1864-...</td>\n",
       "      <td>Zarzuela opereta y ópera en medellín</td>\n",
       "      <td>alejandro morales velez</td>\n",
       "      <td>fondo universidad eafit</td>\n",
       "      <td>...</td>\n",
       "      <td>2012</td>\n",
       "      <td>['alejandro morales velez']</td>\n",
       "      <td>36.0</td>\n",
       "      <td>['martha enna rodriguez melo']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3379</th>\n",
       "      <td>3289</td>\n",
       "      <td>3477</td>\n",
       "      <td>La voz del sabio</td>\n",
       "      <td>1986</td>\n",
       "      <td>laura lee crumley</td>\n",
       "      <td>123</td>\n",
       "      <td>\\n\\t\\t\\t\\t\\tzroara nebura: historias de los an...</td>\n",
       "      <td>Zrõarã Neburã, historia de los antiguos : lite...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>centro jorge eliécer gaitán</td>\n",
       "      <td>...</td>\n",
       "      <td>1984</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['laura lee crumley']</td>\n",
       "      <td>['Catio literature', 'Catio literature -- Tran...</td>\n",
       "      <td>['catio_literature', 'catio_literature_--_tran...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>/books/OL20305803M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3380</th>\n",
       "      <td>3144</td>\n",
       "      <td>3332</td>\n",
       "      <td>Luis Fayad: un presente incierto</td>\n",
       "      <td>1984</td>\n",
       "      <td>alonso aristizabal escobar</td>\n",
       "      <td>319</td>\n",
       "      <td>\\n luis fayad (bogotá, 1945) ha publicado cuat...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>luis fayad</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['luis fayad']</td>\n",
       "      <td>0.0</td>\n",
       "      <td>['alonso aristizabal escobar']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3381 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0.1  Unnamed: 0  \\\n",
       "0              118        3687   \n",
       "1             1221         994   \n",
       "2             1531        1304   \n",
       "3             2531        2344   \n",
       "4             1286        1059   \n",
       "...            ...         ...   \n",
       "3376          3306        3494   \n",
       "3377           769         542   \n",
       "3378          2234        2007   \n",
       "3379          3289        3477   \n",
       "3380          3144        3332   \n",
       "\n",
       "                                             text_title  year  \\\n",
       "0                                     Un libro-mortaja   2021   \n",
       "1                              Dolor, violencia, horror  1995   \n",
       "2     \"Los verdaderos caballeros jamás trabajan\", de...  1998   \n",
       "3                ¡Pffffff...!: una antología desinflada  2004   \n",
       "4                              Un conjunto de anécdotas  2007   \n",
       "...                                                 ...   ...   \n",
       "3376        El mundo como escritura de la fragmentación  2006   \n",
       "3377                        El pavor habita los espejos  1990   \n",
       "3378  Entretelones de ciento cuarenta y cinco años d...  2015   \n",
       "3379                                   La voz del sabio  1986   \n",
       "3380                   Luis Fayad: un presente incierto  1984   \n",
       "\n",
       "                        reviewer  text_id  \\\n",
       "0              mateo navia hoyos   100148   \n",
       "1           dora cecilia ramirez     6028   \n",
       "2             luis h aristizabal     5629   \n",
       "3                   carlos soler     3987   \n",
       "4      adolfo gonzalez henriquez     5955   \n",
       "...                          ...      ...   \n",
       "3376    nelly rocio amaya mendez       94   \n",
       "3377          rafael patiño goez     6645   \n",
       "3378  martha enna rodriguez melo     4700   \n",
       "3379           laura lee crumley      123   \n",
       "3380  alonso aristizabal escobar      319   \n",
       "\n",
       "                                       original_summary  \\\n",
       "0     1989. maría elvira samper. planeta, bogotá, 20...   \n",
       "1     \\n\\t\\t\\t\\t\\t&ellas &ellos, ángela maría pérez ...   \n",
       "2     \\n ...y se hizo la noche sobre ti. enrique dáv...   \n",
       "3     \\n\\t\\t\\t\\t\\t¡aaaaaahhh...! doce relatos erótic...   \n",
       "4     \\n\\t\\t\\t\\t\\t¡baila, negro, baila! crónica de u...   \n",
       "...                                                 ...   \n",
       "3376  \\n\\t\\t\\t\\t\\tzanahorias voladoras. antonio unga...   \n",
       "3377  \\n\\t\\t\\t\\t\\tzarpa y espejo. adolfo león córdob...   \n",
       "3378  \\nZarzuela, opereta y ópera en Medellín, 1864-...   \n",
       "3379  \\n\\t\\t\\t\\t\\tzroara nebura: historias de los an...   \n",
       "3380  \\n luis fayad (bogotá, 1945) ha publicado cuat...   \n",
       "\n",
       "                                             book_title  \\\n",
       "0                                                  1989   \n",
       "1                ellas & ellos: algunas caras femeninas   \n",
       "2                        ...y se hizo la noche sobre ti   \n",
       "3                 ¡aaaaaahhh...! doce relatos eróticos.   \n",
       "4           ¡baila, negro, baila! crónica de un salsero   \n",
       "...                                                 ...   \n",
       "3376                               zanahorias voladoras   \n",
       "3377                                     zarpa y espejo   \n",
       "3378               Zarzuela opereta y ópera en medellín   \n",
       "3379  Zrõarã Neburã, historia de los antiguos : lite...   \n",
       "3380                                                NaN   \n",
       "\n",
       "                           author               book_publisher  ... book_year  \\\n",
       "0             maria elvira samper                      planeta  ...      2019   \n",
       "1      angela maria perez beltran       soluciones editoriales  ...      1994   \n",
       "2         enrique davila martinez                 tercer mundo  ...      1997   \n",
       "3                 varios autores                       planeta  ...      2002   \n",
       "4             rafael araujo gamez            universidad libre  ...      2007   \n",
       "...                           ...                          ...  ...       ...   \n",
       "3376                antonio ungar                    alfaguara  ...      2004   \n",
       "3377  adolfo leon cordoba narvaez   cuadernos de poesía ulrika  ...      1990   \n",
       "3378      alejandro morales velez      fondo universidad eafit  ...      2012   \n",
       "3379                          NaN  centro jorge eliécer gaitán  ...      1984   \n",
       "3380                   luis fayad                          NaN  ...       NaN   \n",
       "\n",
       "                         authors_list len title  \\\n",
       "0             ['maria elvira samper']       4.0   \n",
       "1      ['angela maria perez beltran']      38.0   \n",
       "2         ['enrique davila martinez']      30.0   \n",
       "3                 ['varios autores ']      37.0   \n",
       "4             ['rafael araujo gamez']      43.0   \n",
       "...                               ...       ...   \n",
       "3376                ['antonio ungar']      20.0   \n",
       "3377  ['adolfo leon cordoba narvaez']      14.0   \n",
       "3378      ['alejandro morales velez']      36.0   \n",
       "3379                              NaN       NaN   \n",
       "3380                   ['luis fayad']       0.0   \n",
       "\n",
       "                      reviewers_list  \\\n",
       "0              ['mateo navia hoyos']   \n",
       "1           ['dora cecilia ramirez']   \n",
       "2             ['luis h aristizabal']   \n",
       "3                   ['carlos soler']   \n",
       "4      ['adolfo gonzalez henriquez']   \n",
       "...                              ...   \n",
       "3376    ['nelly rocio amaya mendez']   \n",
       "3377          ['rafael patiño goez']   \n",
       "3378  ['martha enna rodriguez melo']   \n",
       "3379           ['laura lee crumley']   \n",
       "3380  ['alonso aristizabal escobar']   \n",
       "\n",
       "                                                subject  \\\n",
       "0                                                   NaN   \n",
       "1                                                   NaN   \n",
       "2                                                   NaN   \n",
       "3                                                   NaN   \n",
       "4                          ['Salsa (Dance)', 'Fiction']   \n",
       "...                                                 ...   \n",
       "3376                                                NaN   \n",
       "3377                                                NaN   \n",
       "3378                                                NaN   \n",
       "3379  ['Catio literature', 'Catio literature -- Tran...   \n",
       "3380                                                NaN   \n",
       "\n",
       "                                            subject_key  \\\n",
       "0                                                   NaN   \n",
       "1                                                   NaN   \n",
       "2                                                   NaN   \n",
       "3                                                   NaN   \n",
       "4                          ['fiction', 'salsa_(dance)']   \n",
       "...                                                 ...   \n",
       "3376                                                NaN   \n",
       "3377                                                NaN   \n",
       "3378                                                NaN   \n",
       "3379  ['catio_literature', 'catio_literature_--_tran...   \n",
       "3380                                                NaN   \n",
       "\n",
       "                           lcc_sort            lccn id_goodreads  \\\n",
       "0                               NaN             NaN          NaN   \n",
       "1                               NaN             NaN          NaN   \n",
       "2                               NaN    ['97174872']  ['5343112']   \n",
       "3                               NaN             NaN          NaN   \n",
       "4     PQ-8180.10000000.R165 B3 2007  ['2007470232']          NaN   \n",
       "...                             ...             ...          ...   \n",
       "3376                            NaN             NaN          NaN   \n",
       "3377                            NaN             NaN          NaN   \n",
       "3378                            NaN             NaN          NaN   \n",
       "3379                            NaN             NaN          NaN   \n",
       "3380                            NaN             NaN          NaN   \n",
       "\n",
       "            open_library  \n",
       "0     /books/OL44260376M  \n",
       "1                    NaN  \n",
       "2       /books/OL774525M  \n",
       "3                    NaN  \n",
       "4     /books/OL16750076M  \n",
       "...                  ...  \n",
       "3376                 NaN  \n",
       "3377                 NaN  \n",
       "3378                 NaN  \n",
       "3379  /books/OL20305803M  \n",
       "3380                 NaN  \n",
       "\n",
       "[3381 rows x 21 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First, create a dictionary mapping each incorrect variation to the correct location of publisher\n",
    "location_dict = {}\n",
    "for index, row in df_location_publishers.iterrows():\n",
    "    publisher = row['location_publisher']\n",
    "    variations = row['location_variations'].split(',')\n",
    "    for variation in variations:\n",
    "        location_dict[variation.strip()] = publisher\n",
    "\n",
    "# Then, loop through each row of df and replace any incorrect names of locations of publisher with the correct name\n",
    "for index, row in df.iterrows():\n",
    "    location = row['location_publisher']\n",
    "    if location in location_dict:\n",
    "        df.loc[index, 'location_publisher'] = location_dict[location]\n",
    "\n",
    "# show the DataFrame to view the changes\n",
    "(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove duplicates from df_location_publishers based on the 'location_publisher' column\n",
    "df_location_publishers = df_location_publishers.drop_duplicates(subset=[\"location_publisher\"])\n",
    "\n",
    "# Create a dictionary of all publisher names and their variations\n",
    "location_dict = {}\n",
    "for idx, row in df_location_publishers.iterrows():\n",
    "    location = row['location_publisher']\n",
    "    variations = [location]  # Include the publisher name itself\n",
    "    location_dict[location] = variations\n",
    "\n",
    "# Function to check if a publisher name is in a given string\n",
    "def check_publisher_in_string(names, string):\n",
    "    for name in names:\n",
    "        if name in string:\n",
    "            return True\n",
    "    return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize 'books_published' column with NaNs\n",
    "df_location_publishers['books_published'] = np.nan\n",
    "\n",
    "# Loop through each row in df_location_publishers to find IDs of the books published by each publisher\n",
    "for idx, row in df_location_publishers.iterrows():\n",
    "    location = row['location_publisher']\n",
    "    variations = location_dict[location]\n",
    "    published_books = []\n",
    "    for _, review in df.iterrows():\n",
    "        locations = review['location_publisher'].split(',')\n",
    "        locations = [name.strip() for name in locations]\n",
    "        if check_publisher_in_string(variations, review['location_publisher']):\n",
    "            published_books.append(str(review['text_id']))\n",
    "    # Update 'books_published' for the current publisher\n",
    "    df_location_publishers.at[idx, 'books_published'] = ','.join(published_books)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Important: correct the spelling of location of publishers in the reviews dataset.\n",
    "df[\"locations_list\"] = df[\"location_publisher\"].str.split(\",\")\n",
    "\n",
    "# Important: check and correct names from publishers\n",
    "def correct_spelling_locations(publishers_list):\n",
    "    new_location_list = []\n",
    "    for location in publishers_list:\n",
    "        for variations_list, name in zip(df_location_publishers['location_variations'], df_location_publishers['location_publisher']):\n",
    "            if location.strip() in variations_list:\n",
    "                new_location_list.append(name)\n",
    "                break\n",
    "        else:\n",
    "            # If no match is found, keep the original publisher name\n",
    "            new_location_list.append(location)\n",
    "    return new_location_list\n",
    "\n",
    "df['locations_list'] = df[\"locations_list\"].apply(correct_spelling_locations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to check if an actor name is in a given string\n",
    "def check_name_in_string(names, string):\n",
    "    for name in names:\n",
    "        if name in string:\n",
    "            return True\n",
    "    return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new column in df_location_publishers to store the review_years_per_location\n",
    "df_location_publishers['review_years_per_location'] = np.nan\n",
    "\n",
    "# Loop through each row in df_location_publishers and find the review years for each publisher\n",
    "for idx, row in df_location_publishers.iterrows():\n",
    "    location = row['location_publisher']\n",
    "    variations = location_dict[location]\n",
    "    reviews = []\n",
    "    for _, review in df.iterrows():\n",
    "        locations = review['location_publisher'].split(',')\n",
    "        locations = [name.strip() for name in locations]\n",
    "        if check_name_in_string(variations, review['location_publisher']):\n",
    "            reviews.append(str(review['year']))\n",
    "\n",
    "    # Assign the list of review years to the corresponding row in the DataFrame using .loc\n",
    "    df_location_publishers.loc[idx, 'review_years_per_location'] = ','.join(reviews)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location_publisher</th>\n",
       "      <th>location_variations</th>\n",
       "      <th>books_published</th>\n",
       "      <th>review_years_per_location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>universidad externado de colombia</td>\n",
       "      <td>universidad externado de colombia</td>\n",
       "      <td>5958</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>villanueva de la serena espana</td>\n",
       "      <td>espana,villanueva de la serena espana</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>teatro la candelaria</td>\n",
       "      <td>teatro la candelaria</td>\n",
       "      <td>23</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>santa rosa de osos</td>\n",
       "      <td>santa rosa de osos</td>\n",
       "      <td>380</td>\n",
       "      <td>1984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>palma de mallorca</td>\n",
       "      <td>palma de mallorca</td>\n",
       "      <td>5612</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>chia</td>\n",
       "      <td>chia</td>\n",
       "      <td>100010</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>cali</td>\n",
       "      <td>cali,santiago de cali</td>\n",
       "      <td>5955,5795,5669,3260,5926,6264,100142,271,7087,...</td>\n",
       "      <td>2007,1996,1997,2005,1995,1992,2021,1985,1988,2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>faid</td>\n",
       "      <td>faid</td>\n",
       "      <td>6122</td>\n",
       "      <td>1994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>buga</td>\n",
       "      <td>buga</td>\n",
       "      <td>6567</td>\n",
       "      <td>1991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>leon</td>\n",
       "      <td>leon</td>\n",
       "      <td>2896</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>97 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   location_publisher                    location_variations  \\\n",
       "0   universidad externado de colombia      universidad externado de colombia   \n",
       "1      villanueva de la serena espana  espana,villanueva de la serena espana   \n",
       "2                teatro la candelaria                   teatro la candelaria   \n",
       "3                  santa rosa de osos                     santa rosa de osos   \n",
       "4                   palma de mallorca                      palma de mallorca   \n",
       "..                                ...                                    ...   \n",
       "92                               chia                                   chia   \n",
       "93                               cali                  cali,santiago de cali   \n",
       "94                               faid                                   faid   \n",
       "95                               buga                                   buga   \n",
       "96                               leon                                   leon   \n",
       "\n",
       "                                      books_published  \\\n",
       "0                                                5958   \n",
       "1                                                       \n",
       "2                                                  23   \n",
       "3                                                 380   \n",
       "4                                                5612   \n",
       "..                                                ...   \n",
       "92                                             100010   \n",
       "93  5955,5795,5669,3260,5926,6264,100142,271,7087,...   \n",
       "94                                               6122   \n",
       "95                                               6567   \n",
       "96                                               2896   \n",
       "\n",
       "                            review_years_per_location  \n",
       "0                                                2007  \n",
       "1                                                      \n",
       "2                                                2013  \n",
       "3                                                1984  \n",
       "4                                                1998  \n",
       "..                                                ...  \n",
       "92                                               2020  \n",
       "93  2007,1996,1997,2005,1995,1992,2021,1985,1988,2...  \n",
       "94                                               1994  \n",
       "95                                               1991  \n",
       "96                                               2014  \n",
       "\n",
       "[97 rows x 4 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_location_publishers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_715308/4119322834.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_location_publishers[\"review_years_per_location\"] = df_location_publishers[\"review_years_per_location\"].astype(str).str.split(\",\")\n"
     ]
    }
   ],
   "source": [
    "# Remove duplicate rows based on 'location_variations'\n",
    "df_location_publishers = df_location_publishers.drop_duplicates(subset=[\"location_variations\"])\n",
    "\n",
    "# Convert 'reviews_written_years' to lists\n",
    "df_location_publishers[\"review_years_per_location\"] = df_location_publishers[\"review_years_per_location\"].astype(str).str.split(\",\")\n",
    "\n",
    "# Explode the DataFrame on 'review_years_per_location' column\n",
    "df_location_publishers_exploded = df_location_publishers.explode(\"review_years_per_location\")\n",
    "\n",
    "# Filter out rows with empty 'review_years_per_location' entries\n",
    "df_location_publishers_exploded = df_location_publishers_exploded[df_location_publishers_exploded[\"review_years_per_location\"] != \"\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location_publisher</th>\n",
       "      <th>location_variations</th>\n",
       "      <th>books_published</th>\n",
       "      <th>review_years_per_location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>universidad externado de colombia</td>\n",
       "      <td>universidad externado de colombia</td>\n",
       "      <td>5958</td>\n",
       "      <td>[2007]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>villanueva de la serena espana</td>\n",
       "      <td>espana,villanueva de la serena espana</td>\n",
       "      <td></td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>teatro la candelaria</td>\n",
       "      <td>teatro la candelaria</td>\n",
       "      <td>23</td>\n",
       "      <td>[2013]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>santa rosa de osos</td>\n",
       "      <td>santa rosa de osos</td>\n",
       "      <td>380</td>\n",
       "      <td>[1984]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>palma de mallorca</td>\n",
       "      <td>palma de mallorca</td>\n",
       "      <td>5612</td>\n",
       "      <td>[1998]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>neiva</td>\n",
       "      <td>neiva</td>\n",
       "      <td>6650,461,7142</td>\n",
       "      <td>[1990, 2006, 1988]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>chia</td>\n",
       "      <td>chia</td>\n",
       "      <td>100010</td>\n",
       "      <td>[2020]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>faid</td>\n",
       "      <td>faid</td>\n",
       "      <td>6122</td>\n",
       "      <td>[1994]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>buga</td>\n",
       "      <td>buga</td>\n",
       "      <td>6567</td>\n",
       "      <td>[1991]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>leon</td>\n",
       "      <td>leon</td>\n",
       "      <td>2896</td>\n",
       "      <td>[2014]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>94 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   location_publisher                    location_variations  \\\n",
       "0   universidad externado de colombia      universidad externado de colombia   \n",
       "1      villanueva de la serena espana  espana,villanueva de la serena espana   \n",
       "2                teatro la candelaria                   teatro la candelaria   \n",
       "3                  santa rosa de osos                     santa rosa de osos   \n",
       "4                   palma de mallorca                      palma de mallorca   \n",
       "..                                ...                                    ...   \n",
       "91                              neiva                                  neiva   \n",
       "92                               chia                                   chia   \n",
       "94                               faid                                   faid   \n",
       "95                               buga                                   buga   \n",
       "96                               leon                                   leon   \n",
       "\n",
       "   books_published review_years_per_location  \n",
       "0             5958                    [2007]  \n",
       "1                                         []  \n",
       "2               23                    [2013]  \n",
       "3              380                    [1984]  \n",
       "4             5612                    [1998]  \n",
       "..             ...                       ...  \n",
       "91   6650,461,7142        [1990, 2006, 1988]  \n",
       "92          100010                    [2020]  \n",
       "94            6122                    [1994]  \n",
       "95            6567                    [1991]  \n",
       "96            2896                    [2014]  \n",
       "\n",
       "[94 rows x 4 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_location_publishers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# group data by year and gender code, and count the number of occurrences\n",
    "locations_yearly_counts = df_location_publishers_exploded.groupby(['review_years_per_location',\"location_publisher\"]).size()\n",
    "\n",
    "# unstack gender codes index into columns\n",
    "locations_yearly_counts = locations_yearly_counts.unstack()\n",
    "\n",
    "# create blank spaces for missing years\n",
    "missing_years = [\"2000\",\"2009\",\"2010\"]\n",
    "for year in missing_years:\n",
    "    locations_yearly_counts.loc[year] = pd.Series(dtype=int)\n",
    "\n",
    "# sort index and reindex columns\n",
    "locations_yearly_counts.sort_index(inplace=True)\n",
    "top_columns = locations_yearly_counts.sum().nlargest(5).index\n",
    "\n",
    "# plot stacked bar chart\n",
    "ax = locations_yearly_counts.plot(kind='bar', stacked=True)\n",
    "\n",
    "# Modify legend labels to correspond to top columns\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "selected_labels = [label for label in labels if label in top_columns]\n",
    "ax.legend(handles, selected_labels,  bbox_to_anchor=(1.05, 1))\n",
    "\n",
    "# Define colors for the bars\n",
    "colors = plt.cm.tab10.colors[:len(selected_labels)]\n",
    "\n",
    "# Assign colors to bars based on legend labels\n",
    "for label, color in zip(selected_labels, colors):\n",
    "    bars = [bar for bar in ax.containers[0] if bar.get_height() > 0 and label in bar.get_label()]\n",
    "    for bar in bars:\n",
    "        bar.set_color(color)\n",
    "\n",
    "plt.xticks(range(0, len(locations_yearly_counts.index), 2), locations_yearly_counts.index[::2])\n",
    "\n",
    "# add labels and title\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Location count')\n",
    "plt.title('Locations per year')\n",
    "\n",
    "leg =ax.get_legend()\n",
    "leg.legend_handles[0].set_color(\"darkorchid\")\n",
    "leg.legend_handles[1].set_color(\"violet\")\n",
    "leg.legend_handles[2].set_color(\"skyblue\")\n",
    "leg.legend_handles[3].set_color(\"y\")\n",
    "leg.legend_handles[4].set_color(\"red\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>location_publisher</th>\n",
       "      <th>albuquerque</th>\n",
       "      <th>ann arbor</th>\n",
       "      <th>aruba</th>\n",
       "      <th>baltimore</th>\n",
       "      <th>barcelona</th>\n",
       "      <th>barrancabermeja</th>\n",
       "      <th>barranquilla</th>\n",
       "      <th>berlin</th>\n",
       "      <th>berna</th>\n",
       "      <th>bogota</th>\n",
       "      <th>...</th>\n",
       "      <th>universidad externado de colombia</th>\n",
       "      <th>valencia</th>\n",
       "      <th>valledupar</th>\n",
       "      <th>veracruz</th>\n",
       "      <th>villavicencio</th>\n",
       "      <th>washington</th>\n",
       "      <th>wiesbaden</th>\n",
       "      <th>xalapa</th>\n",
       "      <th>yopal</th>\n",
       "      <th>zaragoza</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>review_years_per_location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>74.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>56.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>101.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>84.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>105.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>74.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>39 rows × 91 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "location_publisher         albuquerque  ann arbor  aruba  baltimore  \\\n",
       "review_years_per_location                                             \n",
       "1984                               NaN        NaN    NaN        NaN   \n",
       "1985                               NaN        NaN    NaN        NaN   \n",
       "1986                               NaN        5.0    NaN        NaN   \n",
       "1987                               NaN        4.0    NaN        NaN   \n",
       "1988                               NaN        3.0    NaN        NaN   \n",
       "1989                               NaN        3.0    NaN        1.0   \n",
       "1990                               NaN        NaN    NaN        NaN   \n",
       "1991                               NaN        NaN    NaN        NaN   \n",
       "1992                               NaN        NaN    NaN        NaN   \n",
       "1993                               NaN        NaN    NaN        NaN   \n",
       "1994                               NaN        NaN    NaN        NaN   \n",
       "1995                               NaN        NaN    NaN        NaN   \n",
       "1996                               NaN        NaN    NaN        NaN   \n",
       "1997                               NaN        NaN    NaN        NaN   \n",
       "1998                               NaN        NaN    NaN        NaN   \n",
       "1999                               NaN        NaN    NaN        NaN   \n",
       "2000                               NaN        NaN    NaN        NaN   \n",
       "2001                               1.0        NaN    NaN        NaN   \n",
       "2002                               NaN        NaN    NaN        NaN   \n",
       "2003                               NaN        NaN    NaN        NaN   \n",
       "2004                               NaN        NaN    NaN        NaN   \n",
       "2005                               NaN        NaN    NaN        NaN   \n",
       "2006                               NaN        NaN    NaN        NaN   \n",
       "2007                               NaN        NaN    NaN        NaN   \n",
       "2008                               NaN        NaN    NaN        NaN   \n",
       "2009                               NaN        NaN    NaN        NaN   \n",
       "2010                               NaN        NaN    NaN        NaN   \n",
       "2011                               NaN        NaN    NaN        NaN   \n",
       "2012                               NaN        NaN    NaN        NaN   \n",
       "2013                               NaN        NaN    NaN        NaN   \n",
       "2014                               NaN        NaN    NaN        NaN   \n",
       "2015                               NaN        NaN    NaN        NaN   \n",
       "2016                               NaN        NaN    NaN        NaN   \n",
       "2017                               NaN        NaN    1.0        NaN   \n",
       "2018                               NaN        NaN    NaN        NaN   \n",
       "2019                               NaN        NaN    NaN        NaN   \n",
       "2020                               NaN        NaN    NaN        NaN   \n",
       "2021                               NaN        NaN    NaN        NaN   \n",
       "2022                               NaN        NaN    NaN        NaN   \n",
       "\n",
       "location_publisher         barcelona  barrancabermeja  barranquilla  berlin  \\\n",
       "review_years_per_location                                                     \n",
       "1984                             NaN              NaN           NaN     NaN   \n",
       "1985                             1.0              NaN           NaN     NaN   \n",
       "1986                             NaN              NaN           NaN     NaN   \n",
       "1987                             2.0              NaN           1.0     1.0   \n",
       "1988                             1.0              NaN           2.0     NaN   \n",
       "1989                             NaN              NaN           NaN     NaN   \n",
       "1990                             NaN              NaN           2.0     NaN   \n",
       "1991                             NaN              NaN           2.0     NaN   \n",
       "1992                             1.0              NaN           NaN     NaN   \n",
       "1993                             NaN              NaN           NaN     NaN   \n",
       "1994                             NaN              NaN           1.0     NaN   \n",
       "1995                             NaN              NaN           2.0     NaN   \n",
       "1996                             NaN              NaN           2.0     NaN   \n",
       "1997                             NaN              NaN           1.0     NaN   \n",
       "1998                             1.0              NaN           2.0     NaN   \n",
       "1999                             1.0              NaN           2.0     NaN   \n",
       "2000                             NaN              NaN           NaN     NaN   \n",
       "2001                             1.0              NaN           4.0     NaN   \n",
       "2002                             4.0              NaN           NaN     NaN   \n",
       "2003                             1.0              NaN           2.0     NaN   \n",
       "2004                             2.0              NaN           3.0     NaN   \n",
       "2005                             2.0              NaN           NaN     NaN   \n",
       "2006                             4.0              NaN           2.0     NaN   \n",
       "2007                             NaN              NaN           6.0     NaN   \n",
       "2008                             2.0              NaN           4.0     NaN   \n",
       "2009                             NaN              NaN           NaN     NaN   \n",
       "2010                             NaN              NaN           NaN     NaN   \n",
       "2011                             1.0              NaN           NaN     NaN   \n",
       "2012                             NaN              NaN           1.0     NaN   \n",
       "2013                             3.0              NaN           NaN     NaN   \n",
       "2014                             NaN              1.0           2.0     NaN   \n",
       "2015                             2.0              NaN           2.0     NaN   \n",
       "2016                             1.0              NaN           1.0     NaN   \n",
       "2017                             NaN              NaN           NaN     1.0   \n",
       "2018                             3.0              NaN           NaN     NaN   \n",
       "2019                             NaN              NaN           2.0     NaN   \n",
       "2020                             NaN              NaN           1.0     NaN   \n",
       "2021                             NaN              NaN           NaN     NaN   \n",
       "2022                             NaN              NaN           1.0     NaN   \n",
       "\n",
       "location_publisher         berna  bogota  ...  \\\n",
       "review_years_per_location                 ...   \n",
       "1984                         NaN    40.0  ...   \n",
       "1985                         NaN    50.0  ...   \n",
       "1986                         NaN    64.0  ...   \n",
       "1987                         NaN    69.0  ...   \n",
       "1988                         NaN    94.0  ...   \n",
       "1989                         NaN    93.0  ...   \n",
       "1990                         NaN    91.0  ...   \n",
       "1991                         NaN    62.0  ...   \n",
       "1992                         NaN    74.0  ...   \n",
       "1993                         NaN    62.0  ...   \n",
       "1994                         NaN    50.0  ...   \n",
       "1995                         NaN    51.0  ...   \n",
       "1996                         NaN    66.0  ...   \n",
       "1997                         NaN    70.0  ...   \n",
       "1998                         NaN    67.0  ...   \n",
       "1999                         NaN    89.0  ...   \n",
       "2000                         NaN     NaN  ...   \n",
       "2001                         NaN    61.0  ...   \n",
       "2002                         NaN    54.0  ...   \n",
       "2003                         NaN    56.0  ...   \n",
       "2004                         NaN    97.0  ...   \n",
       "2005                         NaN   101.0  ...   \n",
       "2006                         NaN    53.0  ...   \n",
       "2007                         NaN    84.0  ...   \n",
       "2008                         NaN    31.0  ...   \n",
       "2009                         NaN     NaN  ...   \n",
       "2010                         NaN     NaN  ...   \n",
       "2011                         NaN    58.0  ...   \n",
       "2012                         NaN    58.0  ...   \n",
       "2013                         NaN    23.0  ...   \n",
       "2014                         NaN    53.0  ...   \n",
       "2015                         1.0    91.0  ...   \n",
       "2016                         NaN    63.0  ...   \n",
       "2017                         NaN    25.0  ...   \n",
       "2018                         NaN   105.0  ...   \n",
       "2019                         NaN    57.0  ...   \n",
       "2020                         NaN    74.0  ...   \n",
       "2021                         NaN    72.0  ...   \n",
       "2022                         NaN    32.0  ...   \n",
       "\n",
       "location_publisher         universidad externado de colombia  valencia  \\\n",
       "review_years_per_location                                                \n",
       "1984                                                     NaN       NaN   \n",
       "1985                                                     NaN       NaN   \n",
       "1986                                                     NaN       NaN   \n",
       "1987                                                     NaN       NaN   \n",
       "1988                                                     NaN       NaN   \n",
       "1989                                                     NaN       NaN   \n",
       "1990                                                     NaN       NaN   \n",
       "1991                                                     NaN       NaN   \n",
       "1992                                                     NaN       NaN   \n",
       "1993                                                     NaN       NaN   \n",
       "1994                                                     NaN       NaN   \n",
       "1995                                                     NaN       NaN   \n",
       "1996                                                     NaN       NaN   \n",
       "1997                                                     NaN       NaN   \n",
       "1998                                                     NaN       NaN   \n",
       "1999                                                     NaN       NaN   \n",
       "2000                                                     NaN       NaN   \n",
       "2001                                                     NaN       NaN   \n",
       "2002                                                     NaN       1.0   \n",
       "2003                                                     NaN       NaN   \n",
       "2004                                                     NaN       NaN   \n",
       "2005                                                     NaN       NaN   \n",
       "2006                                                     NaN       2.0   \n",
       "2007                                                     1.0       NaN   \n",
       "2008                                                     NaN       NaN   \n",
       "2009                                                     NaN       NaN   \n",
       "2010                                                     NaN       NaN   \n",
       "2011                                                     NaN       1.0   \n",
       "2012                                                     NaN       NaN   \n",
       "2013                                                     NaN       NaN   \n",
       "2014                                                     NaN       NaN   \n",
       "2015                                                     NaN       1.0   \n",
       "2016                                                     NaN       NaN   \n",
       "2017                                                     NaN       NaN   \n",
       "2018                                                     NaN       NaN   \n",
       "2019                                                     NaN       NaN   \n",
       "2020                                                     NaN       NaN   \n",
       "2021                                                     NaN       NaN   \n",
       "2022                                                     NaN       NaN   \n",
       "\n",
       "location_publisher         valledupar  veracruz  villavicencio  washington  \\\n",
       "review_years_per_location                                                    \n",
       "1984                              NaN       NaN            NaN         NaN   \n",
       "1985                              NaN       NaN            NaN         NaN   \n",
       "1986                              NaN       NaN            NaN         NaN   \n",
       "1987                              NaN       NaN            NaN         NaN   \n",
       "1988                              NaN       NaN            NaN         NaN   \n",
       "1989                              NaN       NaN            NaN         2.0   \n",
       "1990                              NaN       NaN            1.0         NaN   \n",
       "1991                              NaN       NaN            NaN         NaN   \n",
       "1992                              NaN       NaN            NaN         NaN   \n",
       "1993                              NaN       NaN            NaN         1.0   \n",
       "1994                              NaN       NaN            NaN         NaN   \n",
       "1995                              NaN       NaN            NaN         NaN   \n",
       "1996                              NaN       NaN            NaN         NaN   \n",
       "1997                              NaN       NaN            NaN         NaN   \n",
       "1998                              NaN       NaN            NaN         NaN   \n",
       "1999                              NaN       NaN            NaN         NaN   \n",
       "2000                              NaN       NaN            NaN         NaN   \n",
       "2001                              NaN       NaN            NaN         1.0   \n",
       "2002                              NaN       NaN            1.0         NaN   \n",
       "2003                              NaN       NaN            NaN         NaN   \n",
       "2004                              NaN       NaN            1.0         NaN   \n",
       "2005                              NaN       NaN            NaN         NaN   \n",
       "2006                              NaN       NaN            NaN         NaN   \n",
       "2007                              NaN       NaN            NaN         NaN   \n",
       "2008                              NaN       NaN            NaN         NaN   \n",
       "2009                              NaN       NaN            NaN         NaN   \n",
       "2010                              NaN       NaN            NaN         NaN   \n",
       "2011                              NaN       NaN            NaN         NaN   \n",
       "2012                              NaN       NaN            NaN         NaN   \n",
       "2013                              NaN       NaN            NaN         NaN   \n",
       "2014                              NaN       NaN            NaN         1.0   \n",
       "2015                              NaN       NaN            1.0         NaN   \n",
       "2016                              NaN       NaN            1.0         NaN   \n",
       "2017                              NaN       1.0            NaN         NaN   \n",
       "2018                              1.0       NaN            NaN         NaN   \n",
       "2019                              NaN       NaN            NaN         NaN   \n",
       "2020                              NaN       NaN            NaN         NaN   \n",
       "2021                              NaN       NaN            NaN         NaN   \n",
       "2022                              NaN       NaN            NaN         NaN   \n",
       "\n",
       "location_publisher         wiesbaden  xalapa  yopal  zaragoza  \n",
       "review_years_per_location                                      \n",
       "1984                             NaN     NaN    NaN       NaN  \n",
       "1985                             NaN     NaN    NaN       NaN  \n",
       "1986                             NaN     NaN    NaN       NaN  \n",
       "1987                             NaN     NaN    NaN       NaN  \n",
       "1988                             NaN     NaN    NaN       NaN  \n",
       "1989                             NaN     NaN    NaN       NaN  \n",
       "1990                             NaN     NaN    NaN       NaN  \n",
       "1991                             1.0     NaN    NaN       NaN  \n",
       "1992                             NaN     NaN    NaN       NaN  \n",
       "1993                             NaN     NaN    NaN       NaN  \n",
       "1994                             NaN     NaN    NaN       NaN  \n",
       "1995                             NaN     NaN    NaN       NaN  \n",
       "1996                             NaN     NaN    NaN       NaN  \n",
       "1997                             NaN     NaN    NaN       NaN  \n",
       "1998                             NaN     NaN    NaN       NaN  \n",
       "1999                             NaN     NaN    NaN       NaN  \n",
       "2000                             NaN     NaN    NaN       NaN  \n",
       "2001                             NaN     NaN    NaN       NaN  \n",
       "2002                             NaN     NaN    NaN       NaN  \n",
       "2003                             NaN     NaN    NaN       NaN  \n",
       "2004                             NaN     NaN    NaN       NaN  \n",
       "2005                             NaN     NaN    1.0       NaN  \n",
       "2006                             NaN     NaN    NaN       NaN  \n",
       "2007                             NaN     NaN    NaN       NaN  \n",
       "2008                             NaN     NaN    NaN       NaN  \n",
       "2009                             NaN     NaN    NaN       NaN  \n",
       "2010                             NaN     NaN    NaN       NaN  \n",
       "2011                             NaN     NaN    NaN       NaN  \n",
       "2012                             NaN     NaN    NaN       NaN  \n",
       "2013                             NaN     NaN    NaN       NaN  \n",
       "2014                             NaN     NaN    NaN       NaN  \n",
       "2015                             NaN     NaN    NaN       1.0  \n",
       "2016                             NaN     1.0    NaN       NaN  \n",
       "2017                             NaN     NaN    NaN       NaN  \n",
       "2018                             NaN     NaN    NaN       NaN  \n",
       "2019                             NaN     NaN    NaN       NaN  \n",
       "2020                             NaN     NaN    NaN       NaN  \n",
       "2021                             NaN     NaN    NaN       NaN  \n",
       "2022                             NaN     NaN    NaN       NaN  \n",
       "\n",
       "[39 rows x 91 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "locations_yearly_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>location_publisher</th>\n",
       "      <th>bogota</th>\n",
       "      <th>medellin</th>\n",
       "      <th>barranquilla</th>\n",
       "      <th>madrid</th>\n",
       "      <th>barcelona</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>review_years_per_location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>40.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985</th>\n",
       "      <td>50.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>64.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>69.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>94.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>93.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>91.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>62.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>74.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>62.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>50.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>51.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>66.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>70.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>67.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>89.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>61.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>54.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>56.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>97.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>101.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>53.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>84.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>31.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>58.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>58.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>23.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>53.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>91.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>63.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>25.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>105.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>57.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>74.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>72.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>32.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "location_publisher         bogota  medellin  barranquilla  madrid  barcelona\n",
       "review_years_per_location                                                   \n",
       "1984                         40.0       9.0           NaN     1.0        NaN\n",
       "1985                         50.0      12.0           NaN     1.0        1.0\n",
       "1986                         64.0      13.0           NaN     1.0        NaN\n",
       "1987                         69.0      14.0           1.0     1.0        2.0\n",
       "1988                         94.0      16.0           2.0     1.0        1.0\n",
       "1989                         93.0      20.0           NaN     NaN        NaN\n",
       "1990                         91.0      15.0           2.0     1.0        NaN\n",
       "1991                         62.0      24.0           2.0     NaN        NaN\n",
       "1992                         74.0      12.0           NaN     1.0        1.0\n",
       "1993                         62.0       8.0           NaN     1.0        NaN\n",
       "1994                         50.0       8.0           1.0     NaN        NaN\n",
       "1995                         51.0      15.0           2.0     1.0        NaN\n",
       "1996                         66.0       8.0           2.0     NaN        NaN\n",
       "1997                         70.0       2.0           1.0     NaN        NaN\n",
       "1998                         67.0       5.0           2.0     NaN        1.0\n",
       "1999                         89.0      15.0           2.0     NaN        1.0\n",
       "2000                          NaN       NaN           NaN     NaN        NaN\n",
       "2001                         61.0      19.0           4.0     NaN        1.0\n",
       "2002                         54.0      15.0           NaN     1.0        4.0\n",
       "2003                         56.0      21.0           2.0     1.0        1.0\n",
       "2004                         97.0      24.0           3.0     NaN        2.0\n",
       "2005                        101.0      16.0           NaN     1.0        2.0\n",
       "2006                         53.0       8.0           2.0     2.0        4.0\n",
       "2007                         84.0      13.0           6.0     1.0        NaN\n",
       "2008                         31.0      12.0           4.0     1.0        2.0\n",
       "2009                          NaN       NaN           NaN     NaN        NaN\n",
       "2010                          NaN       NaN           NaN     NaN        NaN\n",
       "2011                         58.0       8.0           NaN     1.0        1.0\n",
       "2012                         58.0      13.0           1.0     1.0        NaN\n",
       "2013                         23.0      10.0           NaN     1.0        3.0\n",
       "2014                         53.0      30.0           2.0     3.0        NaN\n",
       "2015                         91.0      29.0           2.0     5.0        2.0\n",
       "2016                         63.0      11.0           1.0     2.0        1.0\n",
       "2017                         25.0       6.0           NaN     1.0        NaN\n",
       "2018                        105.0      18.0           NaN     1.0        3.0\n",
       "2019                         57.0      18.0           2.0     NaN        NaN\n",
       "2020                         74.0      19.0           1.0     NaN        NaN\n",
       "2021                         72.0       8.0           NaN     1.0        NaN\n",
       "2022                         32.0       2.0           1.0     2.0        NaN"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select only the desired columns\n",
    "selected_columns = [\"bogota\", \"medellin\", \"barranquilla\", \"madrid\", \"barcelona\"]\n",
    "selected_data = locations_yearly_counts[selected_columns]\n",
    "selected_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# group data by year and gender code, and count the number of occurrenes\n",
    "locations_yearly_counts = df_location_publishers_exploded.groupby(['review_years_per_location',\"location_publisher\"]).size()\n",
    "\n",
    "# unstack gender codes index into columns\n",
    "locations_yearly_counts = locations_yearly_counts.unstack()\n",
    "\n",
    "\n",
    "# create blank spaces for missing years\n",
    "missing_years = [\"2000\",\"2009\",\"2010\"]\n",
    "for year in missing_years:\n",
    "    locations_yearly_counts.loc[year] = pd.Series(dtype=int)\n",
    "# sort index and reindex columns\n",
    "locations_yearly_counts.sort_index(inplace=True)\n",
    "\n",
    "# plot stacked bar chart\n",
    "locations_yearly_counts.plot(kind='bar', stacked=True)\n",
    "# add labels and title\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of locations')\n",
    "plt.title('locations counts')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Select top 5 columns with largest sums\n",
    "top_columns = locations_yearly_counts.sum().nlargest(5).index\n",
    "\n",
    "# Define colors for each column\n",
    "colors =  ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd']\n",
    "\n",
    "# Plot stacked bar chart with specified colors\n",
    "locations_yearly_counts[top_columns].plot(kind='bar', stacked=True, color=colors)\n",
    "\n",
    "# Set x-axis ticks\n",
    "plt.xticks(range(0, len(locations_yearly_counts.index), 2), locations_yearly_counts.index[::2], rotation=45)\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of publishers')\n",
    "plt.title('Publishers counts')\n",
    "plt.legend(title='Top Publishers', labels=top_columns, loc='upper left')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shannon Diversity Index: 1.8058796514916415\n",
      "Shannon Equitability Index: 0.27749487366068826\n",
      "Simpson Diversity Index: 1.8534873165090335\n",
      "Number of locations: 91\n",
      "Sum of elements included in locations analysis: 3259\n"
     ]
    }
   ],
   "source": [
    "def shannon_diversity_index(counts):\n",
    "    \"\"\"\n",
    "    Calculate Shannon Diversity Index for a given set of counts.\n",
    "\n",
    "    Parameters:\n",
    "    - counts: List of integers representing the number of instances per category.\n",
    "\n",
    "    Returns:\n",
    "    - Shannon Diversity Index (float).\n",
    "    \"\"\"\n",
    "    total_count = sum(counts)\n",
    "    proportions = [count / total_count for count in counts if count > 0]  # Exclude zero counts\n",
    "    shannon_index = -sum(p * math.log2(p) for p in proportions)\n",
    "    \n",
    "    return shannon_index\n",
    "\n",
    "\n",
    "def shannon_equitability_index(counts):\n",
    "    \"\"\"\n",
    "    Calculate Shannon Equitability Index for a given set of counts.\n",
    "\n",
    "    Parameters:\n",
    "    - counts: List of integers representing the number of instances per category.\n",
    "\n",
    "    Returns:\n",
    "    - Shannon Equitability Index (float).\n",
    "    \"\"\"\n",
    "    total_count = sum(counts)\n",
    "    num_categories = len(counts)\n",
    "    max_diversity = math.log2(num_categories)\n",
    "    shannon_index = shannon_diversity_index(counts)\n",
    "    shannon_equitability = shannon_index / max_diversity\n",
    "    \n",
    "    return shannon_equitability\n",
    "\n",
    "\n",
    "def simpson_diversity_index(counts):\n",
    "    \"\"\"\n",
    "    Calculate Simpson Diversity Index for a given set of counts.\n",
    "\n",
    "    Parameters:\n",
    "    - counts: List of integers representing the number of instances per category.\n",
    "\n",
    "    Returns:\n",
    "    - Simpson Diversity Index (float).\n",
    "    \"\"\"\n",
    "    total_count = sum(counts)\n",
    "    probability_squared = [(count / total_count) ** 2 for count in counts]\n",
    "    simpson_index = 1 / sum(probability_squared)\n",
    "    \n",
    "    return simpson_index\n",
    "\n",
    "locations_yearly_counts_sum = locations_yearly_counts.sum()\n",
    "\n",
    "index_shannon_locations = shannon_diversity_index(locations_yearly_counts_sum)\n",
    "print(f\"Shannon Diversity Index: {index_shannon_locations}\")\n",
    "\n",
    "shannon_equitability_locations = shannon_equitability_index(locations_yearly_counts_sum)\n",
    "print(f\"Shannon Equitability Index: {shannon_equitability_locations}\")\n",
    "\n",
    "index_simpson_gender_publishers = simpson_diversity_index(locations_yearly_counts_sum)\n",
    "print(f\"Simpson Diversity Index: {index_simpson_gender_publishers}\")\n",
    "\n",
    "\n",
    "num_elements = locations_yearly_counts.shape[1]\n",
    "print(\"Number of locations:\", num_elements)\n",
    "sum_elements = locations_yearly_counts.applymap(lambda x: int(x) if str(x).strip().replace(\".\", \"\").replace(\"-\", \"\").isdigit() else 0).sum().sum()\n",
    "print(\"Sum of elements included in locations analysis:\", sum_elements)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_715308/588176819.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_location_publishers['num_books_per_location'] = df_location_publishers.apply(count_books_per_location, axis=1)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_books_per_location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>91.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>35.813187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>249.746712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2340.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       num_books_per_location\n",
       "count               91.000000\n",
       "mean                35.813187\n",
       "std                249.746712\n",
       "min                  1.000000\n",
       "25%                  1.000000\n",
       "50%                  1.000000\n",
       "75%                  5.000000\n",
       "max               2340.000000"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define a function to count the number of books per publisher that were reviewed in the BCB.\n",
    "def count_books_per_location(row):\n",
    "    if isinstance(row['books_published'], str) and row[\"books_published\"] != \"\":\n",
    "        return len(row['books_published'].split(','))\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "# Apply the function to create a new column 'num_books_reviews_written'\n",
    "df_location_publishers['num_books_per_location'] = df_location_publishers.apply(count_books_per_location, axis=1)\n",
    "\n",
    "\n",
    "# Drop rows where the number of books published is 0\n",
    "df_location_publishers = df_location_publishers.drop(df_location_publishers[df_location_publishers['num_books_per_location'] == 0].index)\n",
    "\n",
    "# Display descriptive statistics for the remaining data\n",
    "df_location_publishers.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "Empty DataFrame\n",
      "Columns: [location_publisher, location_variations, books_published, review_years_per_location, num_books_per_location]\n",
      "Index: []\n"
     ]
    }
   ],
   "source": [
    "is_in_df_location_publishers = \"universidad de antioquia\" in df_location_publishers[\"location_publisher\"].values\n",
    "print(is_in_df_location_publishers)\n",
    "\n",
    "universidad_de_antioquia_data = df_location_publishers[df_location_publishers[\"location_publisher\"] == \"universidad del rosario\"]\n",
    "print(universidad_de_antioquia_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the total sum of books_published\n",
    "total_books_location_publishers_sum = df_location_publishers['num_books_per_location'].sum()\n",
    "\n",
    "# Calculate the percentage for each row\n",
    "df_location_publishers['books_published_percentage'] = (df_location_publishers['num_books_per_location'] / total_books_location_publishers_sum) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location_publisher</th>\n",
       "      <th>location_variations</th>\n",
       "      <th>books_published</th>\n",
       "      <th>review_years_per_location</th>\n",
       "      <th>num_books_per_location</th>\n",
       "      <th>books_published_percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>bogota</td>\n",
       "      <td>bogota</td>\n",
       "      <td>100148,5629,3987,4761,5398,6304,5288,100040,29...</td>\n",
       "      <td>[2021, 1998, 2004, 2016, 2018, 2018, 1999, 202...</td>\n",
       "      <td>2340</td>\n",
       "      <td>71.801166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>medellin</td>\n",
       "      <td>medellin</td>\n",
       "      <td>5404,5331,1382,2864,7291,6457,2855,371,169,284...</td>\n",
       "      <td>[2018, 2011, 2019, 2014, 1987, 1992, 2014, 198...</td>\n",
       "      <td>496</td>\n",
       "      <td>15.219392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>barranquilla</td>\n",
       "      <td>barranquilla</td>\n",
       "      <td>7189,5137,100100,5829,6416,5944,5039,5619,7074...</td>\n",
       "      <td>[1987, 2001, 2020, 1996, 2007, 2007, 2001, 199...</td>\n",
       "      <td>48</td>\n",
       "      <td>1.472844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>madrid</td>\n",
       "      <td>madrid</td>\n",
       "      <td>6256,4880,4931,7040,6204,5966,4613,196,5519,14...</td>\n",
       "      <td>[1992, 2011, 2002, 1988, 1993, 2007, 2015, 198...</td>\n",
       "      <td>34</td>\n",
       "      <td>1.043265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>barcelona</td>\n",
       "      <td>barcelona</td>\n",
       "      <td>96,4179,4556,5544,5047,14,98,453,5536,5328,720...</td>\n",
       "      <td>[2006, 2004, 2002, 2008, 2001, 2013, 2006, 200...</td>\n",
       "      <td>33</td>\n",
       "      <td>1.012581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>manizales</td>\n",
       "      <td>manizales</td>\n",
       "      <td>18,5832,2297,5322,124,4932,4999,4662,4195,4959...</td>\n",
       "      <td>[2013, 1996, 2012, 2011, 1986, 2002, 2017, 201...</td>\n",
       "      <td>32</td>\n",
       "      <td>0.981896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>mexico</td>\n",
       "      <td>mexico</td>\n",
       "      <td>6037,4432,128,34,312,2393,5358,7102,5690,5246,...</td>\n",
       "      <td>[1995, 2003, 1986, 2013, 1985, 2014, 1999, 198...</td>\n",
       "      <td>30</td>\n",
       "      <td>0.920528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>bucaramanga</td>\n",
       "      <td>bucaramanga</td>\n",
       "      <td>4665,4603,2874,6523,2306,2862,27,4794,75,5249,...</td>\n",
       "      <td>[2015, 2015, 2014, 1991, 2012, 2014, 2013, 201...</td>\n",
       "      <td>25</td>\n",
       "      <td>0.767106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>ibague</td>\n",
       "      <td>ibague</td>\n",
       "      <td>5406,6551,6851,4777,5906,457,4646,4598,4772,72...</td>\n",
       "      <td>[2018, 1991, 1989, 2016, 1995, 2006, 2015, 201...</td>\n",
       "      <td>19</td>\n",
       "      <td>0.583001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>buenos aires</td>\n",
       "      <td>buenos aires</td>\n",
       "      <td>4838,289,5519,7274,6795,379,2668,7137,160,7133...</td>\n",
       "      <td>[2016, 1985, 2008, 1987, 1989, 1984, 2012, 198...</td>\n",
       "      <td>16</td>\n",
       "      <td>0.490948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>ann arbor</td>\n",
       "      <td>ann arbor</td>\n",
       "      <td>6948,7195,7163,7316,7314,7036,7309,7111,204,71...</td>\n",
       "      <td>[1989, 1987, 1987, 1986, 1986, 1988, 1986, 198...</td>\n",
       "      <td>15</td>\n",
       "      <td>0.460264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>cartagena</td>\n",
       "      <td>cartagena</td>\n",
       "      <td>45,6461,4690,2847,6699,2333,71,5905,5884,53,58...</td>\n",
       "      <td>[2013, 1992, 2015, 2014, 1990, 2012, 2006, 199...</td>\n",
       "      <td>12</td>\n",
       "      <td>0.368211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>caracas</td>\n",
       "      <td>caracas</td>\n",
       "      <td>6460,6459,2398,5882,377,2294,206,4465,6200,5859</td>\n",
       "      <td>[1992, 1992, 2014, 1995, 1984, 2012, 1986, 200...</td>\n",
       "      <td>10</td>\n",
       "      <td>0.306843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>tunja</td>\n",
       "      <td>tunja</td>\n",
       "      <td>5999,7165,5528,2387,6781,4309,6694,3993</td>\n",
       "      <td>[2007, 1987, 2008, 2014, 1989, 2003, 1990, 2004]</td>\n",
       "      <td>8</td>\n",
       "      <td>0.245474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>santa marta</td>\n",
       "      <td>santa marta</td>\n",
       "      <td>100045,6535,21,2683,100221,3493,692</td>\n",
       "      <td>[2020, 1991, 2013, 2012, 2021, 2005, 2005]</td>\n",
       "      <td>7</td>\n",
       "      <td>0.214790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>pereira</td>\n",
       "      <td>pereira</td>\n",
       "      <td>6073,4722,3999,5008,4887,3998,193</td>\n",
       "      <td>[1994, 2015, 2004, 2017, 2011, 2004, 1986]</td>\n",
       "      <td>7</td>\n",
       "      <td>0.214790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>nueva york</td>\n",
       "      <td>nueva york</td>\n",
       "      <td>678,1367,7204,3521,3522,6550</td>\n",
       "      <td>[2005, 2019, 1987, 2005, 2005, 1991]</td>\n",
       "      <td>6</td>\n",
       "      <td>0.184106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>monteria</td>\n",
       "      <td>monteria</td>\n",
       "      <td>5976,6164,1363,6643,7213,3766</td>\n",
       "      <td>[2007, 1993, 2019, 1990, 1987, 2004]</td>\n",
       "      <td>6</td>\n",
       "      <td>0.184106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>villavicencio</td>\n",
       "      <td>villavicencio</td>\n",
       "      <td>6720,4799,4957,4702,3770</td>\n",
       "      <td>[1990, 2016, 2002, 2015, 2004]</td>\n",
       "      <td>5</td>\n",
       "      <td>0.153421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>washington</td>\n",
       "      <td>washington</td>\n",
       "      <td>2845,6218,5083,6863,6826</td>\n",
       "      <td>[2014, 1993, 2001, 1989, 1989]</td>\n",
       "      <td>5</td>\n",
       "      <td>0.153421</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   location_publisher location_variations  \\\n",
       "59             bogota              bogota   \n",
       "37           medellin            medellin   \n",
       "9        barranquilla        barranquilla   \n",
       "63             madrid              madrid   \n",
       "24          barcelona           barcelona   \n",
       "27          manizales           manizales   \n",
       "69             mexico              mexico   \n",
       "13        bucaramanga         bucaramanga   \n",
       "66             ibague              ibague   \n",
       "10       buenos aires        buenos aires   \n",
       "31          ann arbor           ann arbor   \n",
       "25          cartagena           cartagena   \n",
       "57            caracas             caracas   \n",
       "89              tunja               tunja   \n",
       "12        santa marta         santa marta   \n",
       "48            pereira             pereira   \n",
       "17         nueva york          nueva york   \n",
       "45           monteria            monteria   \n",
       "8       villavicencio       villavicencio   \n",
       "16         washington          washington   \n",
       "\n",
       "                                      books_published  \\\n",
       "59  100148,5629,3987,4761,5398,6304,5288,100040,29...   \n",
       "37  5404,5331,1382,2864,7291,6457,2855,371,169,284...   \n",
       "9   7189,5137,100100,5829,6416,5944,5039,5619,7074...   \n",
       "63  6256,4880,4931,7040,6204,5966,4613,196,5519,14...   \n",
       "24  96,4179,4556,5544,5047,14,98,453,5536,5328,720...   \n",
       "27  18,5832,2297,5322,124,4932,4999,4662,4195,4959...   \n",
       "69  6037,4432,128,34,312,2393,5358,7102,5690,5246,...   \n",
       "13  4665,4603,2874,6523,2306,2862,27,4794,75,5249,...   \n",
       "66  5406,6551,6851,4777,5906,457,4646,4598,4772,72...   \n",
       "10  4838,289,5519,7274,6795,379,2668,7137,160,7133...   \n",
       "31  6948,7195,7163,7316,7314,7036,7309,7111,204,71...   \n",
       "25  45,6461,4690,2847,6699,2333,71,5905,5884,53,58...   \n",
       "57    6460,6459,2398,5882,377,2294,206,4465,6200,5859   \n",
       "89            5999,7165,5528,2387,6781,4309,6694,3993   \n",
       "12                100045,6535,21,2683,100221,3493,692   \n",
       "48                  6073,4722,3999,5008,4887,3998,193   \n",
       "17                       678,1367,7204,3521,3522,6550   \n",
       "45                      5976,6164,1363,6643,7213,3766   \n",
       "8                            6720,4799,4957,4702,3770   \n",
       "16                           2845,6218,5083,6863,6826   \n",
       "\n",
       "                            review_years_per_location  num_books_per_location  \\\n",
       "59  [2021, 1998, 2004, 2016, 2018, 2018, 1999, 202...                    2340   \n",
       "37  [2018, 2011, 2019, 2014, 1987, 1992, 2014, 198...                     496   \n",
       "9   [1987, 2001, 2020, 1996, 2007, 2007, 2001, 199...                      48   \n",
       "63  [1992, 2011, 2002, 1988, 1993, 2007, 2015, 198...                      34   \n",
       "24  [2006, 2004, 2002, 2008, 2001, 2013, 2006, 200...                      33   \n",
       "27  [2013, 1996, 2012, 2011, 1986, 2002, 2017, 201...                      32   \n",
       "69  [1995, 2003, 1986, 2013, 1985, 2014, 1999, 198...                      30   \n",
       "13  [2015, 2015, 2014, 1991, 2012, 2014, 2013, 201...                      25   \n",
       "66  [2018, 1991, 1989, 2016, 1995, 2006, 2015, 201...                      19   \n",
       "10  [2016, 1985, 2008, 1987, 1989, 1984, 2012, 198...                      16   \n",
       "31  [1989, 1987, 1987, 1986, 1986, 1988, 1986, 198...                      15   \n",
       "25  [2013, 1992, 2015, 2014, 1990, 2012, 2006, 199...                      12   \n",
       "57  [1992, 1992, 2014, 1995, 1984, 2012, 1986, 200...                      10   \n",
       "89   [2007, 1987, 2008, 2014, 1989, 2003, 1990, 2004]                       8   \n",
       "12         [2020, 1991, 2013, 2012, 2021, 2005, 2005]                       7   \n",
       "48         [1994, 2015, 2004, 2017, 2011, 2004, 1986]                       7   \n",
       "17               [2005, 2019, 1987, 2005, 2005, 1991]                       6   \n",
       "45               [2007, 1993, 2019, 1990, 1987, 2004]                       6   \n",
       "8                      [1990, 2016, 2002, 2015, 2004]                       5   \n",
       "16                     [2014, 1993, 2001, 1989, 1989]                       5   \n",
       "\n",
       "    books_published_percentage  \n",
       "59                   71.801166  \n",
       "37                   15.219392  \n",
       "9                     1.472844  \n",
       "63                    1.043265  \n",
       "24                    1.012581  \n",
       "27                    0.981896  \n",
       "69                    0.920528  \n",
       "13                    0.767106  \n",
       "66                    0.583001  \n",
       "10                    0.490948  \n",
       "31                    0.460264  \n",
       "25                    0.368211  \n",
       "57                    0.306843  \n",
       "89                    0.245474  \n",
       "12                    0.214790  \n",
       "48                    0.214790  \n",
       "17                    0.184106  \n",
       "45                    0.184106  \n",
       "8                     0.153421  \n",
       "16                    0.153421  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find top 20 publishers by quantity of books published.\n",
    "top_20_locations = df_location_publishers.nlargest(20, \"num_books_per_location\")\n",
    "#top_20_locations = top_20_locations.reindex(columns=['name', 'name_variations', 'gender', 'num_books_written', 'books_written_ids'])\n",
    "\n",
    "# Display top 20 publishers\n",
    "(top_20_locations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regression Equation: y = 0.0013x + -2.1297\n",
      "Slope (m): 0.0013\n",
      "Intercept (b): -2.1297\n",
      "R-squared: 0.0283\n",
      "p-value: 0.3266\n",
      "Standard Error: 0.0013\n"
     ]
    }
   ],
   "source": [
    "counts_locations_per_year = df_location_publishers_exploded.groupby('review_years_per_location')['location_publisher'].value_counts()\n",
    "shannon_equitability_locations = counts_locations_per_year.groupby(level='review_years_per_location').apply(lambda x: shannon_equitability_index(x))\n",
    "shannon_equitability_locations.loc[\"2000\"]= np.nan\n",
    "shannon_equitability_locations.loc[\"2009\"]= np.nan\n",
    "shannon_equitability_locations.loc[\"2010\"]= np.nan\n",
    "shannon_equitability_locations= shannon_equitability_locations.sort_index()\n",
    "\n",
    "# Convert index to strings\n",
    "shannon_equitability_locations.index = shannon_equitability_locations.index.astype(str)\n",
    "\n",
    "shannon_equitability_locations= shannon_equitability_locations.sort_index()\n",
    "\n",
    "# Drop NaN values for regression calculation\n",
    "shannon_equitability_locations_cleaned = shannon_equitability_locations.dropna()\n",
    "\n",
    "# Perform linear regression\n",
    "x = shannon_equitability_locations_cleaned.index.astype(int)\n",
    "y = shannon_equitability_locations_cleaned.values\n",
    "coefficients = np.polyfit(x, y, 1)\n",
    "poly = np.poly1d(coefficients)\n",
    "trend_x = np.array([min(x), max(x)])\n",
    "trend_y = poly(trend_x)\n",
    "# Group data by year and gender code, and count the number of occurrences\n",
    "locations_yearly_counts = df_location_publishers_exploded.groupby(['review_years_per_location', 'location_publisher']).size()\n",
    "\n",
    "# Unstack gender codes index into columns\n",
    "locations_yearly_counts = locations_yearly_counts.unstack()\n",
    "\n",
    "# Create blank spaces for missing years\n",
    "missing_years = [\"2000\", \"2009\", \"2010\"]\n",
    "for year in missing_years:\n",
    "    locations_yearly_counts.loc[year] = pd.Series(dtype=int)\n",
    "\n",
    "# Sort index and reindex columns\n",
    "locations_yearly_counts.sort_index(inplace=True)\n",
    "\n",
    "# Select top 5 columns with largest sums\n",
    "top_columns = locations_yearly_counts.sum().nlargest(5).index\n",
    "\n",
    "# Define colors for each column\n",
    "colors =  ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd']\n",
    "\n",
    "# Plot stacked bar chart with specified colors\n",
    "locations_yearly_counts[top_columns].plot(kind='bar', stacked=True, color=colors)\n",
    "\n",
    "# Set x-axis ticks\n",
    "plt.xticks(range(0, len(locations_yearly_counts.index), 2), locations_yearly_counts.index[::2], rotation=45)\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of publishers')\n",
    "plt.title('Publishers counts')\n",
    "plt.legend(title='Top Publishers', labels=top_columns, loc='upper left')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "# Plot the linear regression line\n",
    "plt.plot(trend_x, trend_y, color='red', linestyle='--', label='Linear Regression')\n",
    "\n",
    "# Plot yearly Shannon equitability results\n",
    "plt.scatter(x, y, color='blue', label='Yearly Results')\n",
    "\n",
    "plt.title('Linear Regression of Shannon Equitability: location of publication')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Shannon Equitability Index')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# Perform linear regression using stats.linregress\n",
    "slope, intercept, r_value, p_value, std_err = stats.linregress(x, y)\n",
    "\n",
    "# Compute R-squared\n",
    "r_squared = r_value ** 2\n",
    "\n",
    "# Print regression statistics\n",
    "print(\"Linear Regression Equation: y = {:.4f}x + {:.4f}\".format(slope, intercept))\n",
    "print(\"Slope (m): {:.4f}\".format(slope))\n",
    "print(\"Intercept (b): {:.4f}\".format(intercept))\n",
    "print(\"R-squared: {:.4f}\".format(r_squared))\n",
    "print(\"p-value: {:.4f}\".format(p_value))\n",
    "print(\"Standard Error: {:.4f}\".format(std_err))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bogota': '#1f77b4',\n",
       " 'medellin': '#ff7f0e',\n",
       " 'barranquilla': '#2ca02c',\n",
       " 'madrid': '#d62728',\n",
       " 'barcelona': '#9467bd'}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "color_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>location_publisher</th>\n",
       "      <th>albuquerque</th>\n",
       "      <th>ann arbor</th>\n",
       "      <th>aruba</th>\n",
       "      <th>baltimore</th>\n",
       "      <th>barcelona</th>\n",
       "      <th>barrancabermeja</th>\n",
       "      <th>barranquilla</th>\n",
       "      <th>berlin</th>\n",
       "      <th>berna</th>\n",
       "      <th>bogota</th>\n",
       "      <th>...</th>\n",
       "      <th>universidad externado de colombia</th>\n",
       "      <th>valencia</th>\n",
       "      <th>valledupar</th>\n",
       "      <th>veracruz</th>\n",
       "      <th>villavicencio</th>\n",
       "      <th>washington</th>\n",
       "      <th>wiesbaden</th>\n",
       "      <th>xalapa</th>\n",
       "      <th>yopal</th>\n",
       "      <th>zaragoza</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>review_years_per_location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>74.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>56.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>101.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>84.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>105.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>74.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>39 rows × 91 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "location_publisher         albuquerque  ann arbor  aruba  baltimore  \\\n",
       "review_years_per_location                                             \n",
       "1984                               NaN        NaN    NaN        NaN   \n",
       "1985                               NaN        NaN    NaN        NaN   \n",
       "1986                               NaN        5.0    NaN        NaN   \n",
       "1987                               NaN        4.0    NaN        NaN   \n",
       "1988                               NaN        3.0    NaN        NaN   \n",
       "1989                               NaN        3.0    NaN        1.0   \n",
       "1990                               NaN        NaN    NaN        NaN   \n",
       "1991                               NaN        NaN    NaN        NaN   \n",
       "1992                               NaN        NaN    NaN        NaN   \n",
       "1993                               NaN        NaN    NaN        NaN   \n",
       "1994                               NaN        NaN    NaN        NaN   \n",
       "1995                               NaN        NaN    NaN        NaN   \n",
       "1996                               NaN        NaN    NaN        NaN   \n",
       "1997                               NaN        NaN    NaN        NaN   \n",
       "1998                               NaN        NaN    NaN        NaN   \n",
       "1999                               NaN        NaN    NaN        NaN   \n",
       "2000                               NaN        NaN    NaN        NaN   \n",
       "2001                               1.0        NaN    NaN        NaN   \n",
       "2002                               NaN        NaN    NaN        NaN   \n",
       "2003                               NaN        NaN    NaN        NaN   \n",
       "2004                               NaN        NaN    NaN        NaN   \n",
       "2005                               NaN        NaN    NaN        NaN   \n",
       "2006                               NaN        NaN    NaN        NaN   \n",
       "2007                               NaN        NaN    NaN        NaN   \n",
       "2008                               NaN        NaN    NaN        NaN   \n",
       "2009                               NaN        NaN    NaN        NaN   \n",
       "2010                               NaN        NaN    NaN        NaN   \n",
       "2011                               NaN        NaN    NaN        NaN   \n",
       "2012                               NaN        NaN    NaN        NaN   \n",
       "2013                               NaN        NaN    NaN        NaN   \n",
       "2014                               NaN        NaN    NaN        NaN   \n",
       "2015                               NaN        NaN    NaN        NaN   \n",
       "2016                               NaN        NaN    NaN        NaN   \n",
       "2017                               NaN        NaN    1.0        NaN   \n",
       "2018                               NaN        NaN    NaN        NaN   \n",
       "2019                               NaN        NaN    NaN        NaN   \n",
       "2020                               NaN        NaN    NaN        NaN   \n",
       "2021                               NaN        NaN    NaN        NaN   \n",
       "2022                               NaN        NaN    NaN        NaN   \n",
       "\n",
       "location_publisher         barcelona  barrancabermeja  barranquilla  berlin  \\\n",
       "review_years_per_location                                                     \n",
       "1984                             NaN              NaN           NaN     NaN   \n",
       "1985                             1.0              NaN           NaN     NaN   \n",
       "1986                             NaN              NaN           NaN     NaN   \n",
       "1987                             2.0              NaN           1.0     1.0   \n",
       "1988                             1.0              NaN           2.0     NaN   \n",
       "1989                             NaN              NaN           NaN     NaN   \n",
       "1990                             NaN              NaN           2.0     NaN   \n",
       "1991                             NaN              NaN           2.0     NaN   \n",
       "1992                             1.0              NaN           NaN     NaN   \n",
       "1993                             NaN              NaN           NaN     NaN   \n",
       "1994                             NaN              NaN           1.0     NaN   \n",
       "1995                             NaN              NaN           2.0     NaN   \n",
       "1996                             NaN              NaN           2.0     NaN   \n",
       "1997                             NaN              NaN           1.0     NaN   \n",
       "1998                             1.0              NaN           2.0     NaN   \n",
       "1999                             1.0              NaN           2.0     NaN   \n",
       "2000                             NaN              NaN           NaN     NaN   \n",
       "2001                             1.0              NaN           4.0     NaN   \n",
       "2002                             4.0              NaN           NaN     NaN   \n",
       "2003                             1.0              NaN           2.0     NaN   \n",
       "2004                             2.0              NaN           3.0     NaN   \n",
       "2005                             2.0              NaN           NaN     NaN   \n",
       "2006                             4.0              NaN           2.0     NaN   \n",
       "2007                             NaN              NaN           6.0     NaN   \n",
       "2008                             2.0              NaN           4.0     NaN   \n",
       "2009                             NaN              NaN           NaN     NaN   \n",
       "2010                             NaN              NaN           NaN     NaN   \n",
       "2011                             1.0              NaN           NaN     NaN   \n",
       "2012                             NaN              NaN           1.0     NaN   \n",
       "2013                             3.0              NaN           NaN     NaN   \n",
       "2014                             NaN              1.0           2.0     NaN   \n",
       "2015                             2.0              NaN           2.0     NaN   \n",
       "2016                             1.0              NaN           1.0     NaN   \n",
       "2017                             NaN              NaN           NaN     1.0   \n",
       "2018                             3.0              NaN           NaN     NaN   \n",
       "2019                             NaN              NaN           2.0     NaN   \n",
       "2020                             NaN              NaN           1.0     NaN   \n",
       "2021                             NaN              NaN           NaN     NaN   \n",
       "2022                             NaN              NaN           1.0     NaN   \n",
       "\n",
       "location_publisher         berna  bogota  ...  \\\n",
       "review_years_per_location                 ...   \n",
       "1984                         NaN    40.0  ...   \n",
       "1985                         NaN    50.0  ...   \n",
       "1986                         NaN    64.0  ...   \n",
       "1987                         NaN    69.0  ...   \n",
       "1988                         NaN    94.0  ...   \n",
       "1989                         NaN    93.0  ...   \n",
       "1990                         NaN    91.0  ...   \n",
       "1991                         NaN    62.0  ...   \n",
       "1992                         NaN    74.0  ...   \n",
       "1993                         NaN    62.0  ...   \n",
       "1994                         NaN    50.0  ...   \n",
       "1995                         NaN    51.0  ...   \n",
       "1996                         NaN    66.0  ...   \n",
       "1997                         NaN    70.0  ...   \n",
       "1998                         NaN    67.0  ...   \n",
       "1999                         NaN    89.0  ...   \n",
       "2000                         NaN     NaN  ...   \n",
       "2001                         NaN    61.0  ...   \n",
       "2002                         NaN    54.0  ...   \n",
       "2003                         NaN    56.0  ...   \n",
       "2004                         NaN    97.0  ...   \n",
       "2005                         NaN   101.0  ...   \n",
       "2006                         NaN    53.0  ...   \n",
       "2007                         NaN    84.0  ...   \n",
       "2008                         NaN    31.0  ...   \n",
       "2009                         NaN     NaN  ...   \n",
       "2010                         NaN     NaN  ...   \n",
       "2011                         NaN    58.0  ...   \n",
       "2012                         NaN    58.0  ...   \n",
       "2013                         NaN    23.0  ...   \n",
       "2014                         NaN    53.0  ...   \n",
       "2015                         1.0    91.0  ...   \n",
       "2016                         NaN    63.0  ...   \n",
       "2017                         NaN    25.0  ...   \n",
       "2018                         NaN   105.0  ...   \n",
       "2019                         NaN    57.0  ...   \n",
       "2020                         NaN    74.0  ...   \n",
       "2021                         NaN    72.0  ...   \n",
       "2022                         NaN    32.0  ...   \n",
       "\n",
       "location_publisher         universidad externado de colombia  valencia  \\\n",
       "review_years_per_location                                                \n",
       "1984                                                     NaN       NaN   \n",
       "1985                                                     NaN       NaN   \n",
       "1986                                                     NaN       NaN   \n",
       "1987                                                     NaN       NaN   \n",
       "1988                                                     NaN       NaN   \n",
       "1989                                                     NaN       NaN   \n",
       "1990                                                     NaN       NaN   \n",
       "1991                                                     NaN       NaN   \n",
       "1992                                                     NaN       NaN   \n",
       "1993                                                     NaN       NaN   \n",
       "1994                                                     NaN       NaN   \n",
       "1995                                                     NaN       NaN   \n",
       "1996                                                     NaN       NaN   \n",
       "1997                                                     NaN       NaN   \n",
       "1998                                                     NaN       NaN   \n",
       "1999                                                     NaN       NaN   \n",
       "2000                                                     NaN       NaN   \n",
       "2001                                                     NaN       NaN   \n",
       "2002                                                     NaN       1.0   \n",
       "2003                                                     NaN       NaN   \n",
       "2004                                                     NaN       NaN   \n",
       "2005                                                     NaN       NaN   \n",
       "2006                                                     NaN       2.0   \n",
       "2007                                                     1.0       NaN   \n",
       "2008                                                     NaN       NaN   \n",
       "2009                                                     NaN       NaN   \n",
       "2010                                                     NaN       NaN   \n",
       "2011                                                     NaN       1.0   \n",
       "2012                                                     NaN       NaN   \n",
       "2013                                                     NaN       NaN   \n",
       "2014                                                     NaN       NaN   \n",
       "2015                                                     NaN       1.0   \n",
       "2016                                                     NaN       NaN   \n",
       "2017                                                     NaN       NaN   \n",
       "2018                                                     NaN       NaN   \n",
       "2019                                                     NaN       NaN   \n",
       "2020                                                     NaN       NaN   \n",
       "2021                                                     NaN       NaN   \n",
       "2022                                                     NaN       NaN   \n",
       "\n",
       "location_publisher         valledupar  veracruz  villavicencio  washington  \\\n",
       "review_years_per_location                                                    \n",
       "1984                              NaN       NaN            NaN         NaN   \n",
       "1985                              NaN       NaN            NaN         NaN   \n",
       "1986                              NaN       NaN            NaN         NaN   \n",
       "1987                              NaN       NaN            NaN         NaN   \n",
       "1988                              NaN       NaN            NaN         NaN   \n",
       "1989                              NaN       NaN            NaN         2.0   \n",
       "1990                              NaN       NaN            1.0         NaN   \n",
       "1991                              NaN       NaN            NaN         NaN   \n",
       "1992                              NaN       NaN            NaN         NaN   \n",
       "1993                              NaN       NaN            NaN         1.0   \n",
       "1994                              NaN       NaN            NaN         NaN   \n",
       "1995                              NaN       NaN            NaN         NaN   \n",
       "1996                              NaN       NaN            NaN         NaN   \n",
       "1997                              NaN       NaN            NaN         NaN   \n",
       "1998                              NaN       NaN            NaN         NaN   \n",
       "1999                              NaN       NaN            NaN         NaN   \n",
       "2000                              NaN       NaN            NaN         NaN   \n",
       "2001                              NaN       NaN            NaN         1.0   \n",
       "2002                              NaN       NaN            1.0         NaN   \n",
       "2003                              NaN       NaN            NaN         NaN   \n",
       "2004                              NaN       NaN            1.0         NaN   \n",
       "2005                              NaN       NaN            NaN         NaN   \n",
       "2006                              NaN       NaN            NaN         NaN   \n",
       "2007                              NaN       NaN            NaN         NaN   \n",
       "2008                              NaN       NaN            NaN         NaN   \n",
       "2009                              NaN       NaN            NaN         NaN   \n",
       "2010                              NaN       NaN            NaN         NaN   \n",
       "2011                              NaN       NaN            NaN         NaN   \n",
       "2012                              NaN       NaN            NaN         NaN   \n",
       "2013                              NaN       NaN            NaN         NaN   \n",
       "2014                              NaN       NaN            NaN         1.0   \n",
       "2015                              NaN       NaN            1.0         NaN   \n",
       "2016                              NaN       NaN            1.0         NaN   \n",
       "2017                              NaN       1.0            NaN         NaN   \n",
       "2018                              1.0       NaN            NaN         NaN   \n",
       "2019                              NaN       NaN            NaN         NaN   \n",
       "2020                              NaN       NaN            NaN         NaN   \n",
       "2021                              NaN       NaN            NaN         NaN   \n",
       "2022                              NaN       NaN            NaN         NaN   \n",
       "\n",
       "location_publisher         wiesbaden  xalapa  yopal  zaragoza  \n",
       "review_years_per_location                                      \n",
       "1984                             NaN     NaN    NaN       NaN  \n",
       "1985                             NaN     NaN    NaN       NaN  \n",
       "1986                             NaN     NaN    NaN       NaN  \n",
       "1987                             NaN     NaN    NaN       NaN  \n",
       "1988                             NaN     NaN    NaN       NaN  \n",
       "1989                             NaN     NaN    NaN       NaN  \n",
       "1990                             NaN     NaN    NaN       NaN  \n",
       "1991                             1.0     NaN    NaN       NaN  \n",
       "1992                             NaN     NaN    NaN       NaN  \n",
       "1993                             NaN     NaN    NaN       NaN  \n",
       "1994                             NaN     NaN    NaN       NaN  \n",
       "1995                             NaN     NaN    NaN       NaN  \n",
       "1996                             NaN     NaN    NaN       NaN  \n",
       "1997                             NaN     NaN    NaN       NaN  \n",
       "1998                             NaN     NaN    NaN       NaN  \n",
       "1999                             NaN     NaN    NaN       NaN  \n",
       "2000                             NaN     NaN    NaN       NaN  \n",
       "2001                             NaN     NaN    NaN       NaN  \n",
       "2002                             NaN     NaN    NaN       NaN  \n",
       "2003                             NaN     NaN    NaN       NaN  \n",
       "2004                             NaN     NaN    NaN       NaN  \n",
       "2005                             NaN     NaN    1.0       NaN  \n",
       "2006                             NaN     NaN    NaN       NaN  \n",
       "2007                             NaN     NaN    NaN       NaN  \n",
       "2008                             NaN     NaN    NaN       NaN  \n",
       "2009                             NaN     NaN    NaN       NaN  \n",
       "2010                             NaN     NaN    NaN       NaN  \n",
       "2011                             NaN     NaN    NaN       NaN  \n",
       "2012                             NaN     NaN    NaN       NaN  \n",
       "2013                             NaN     NaN    NaN       NaN  \n",
       "2014                             NaN     NaN    NaN       NaN  \n",
       "2015                             NaN     NaN    NaN       1.0  \n",
       "2016                             NaN     1.0    NaN       NaN  \n",
       "2017                             NaN     NaN    NaN       NaN  \n",
       "2018                             NaN     NaN    NaN       NaN  \n",
       "2019                             NaN     NaN    NaN       NaN  \n",
       "2020                             NaN     NaN    NaN       NaN  \n",
       "2021                             NaN     NaN    NaN       NaN  \n",
       "2022                             NaN     NaN    NaN       NaN  \n",
       "\n",
       "[39 rows x 91 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(locations_yearly_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['bogota', 'medellin', 'barranquilla', 'madrid', 'barcelona'], dtype='object', name='location_publisher')\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "# Get all locations from columns\n",
    "all_locations = locations_yearly_counts.columns\n",
    "\n",
    "# Create a dictionary to assign unique colors to each location\n",
    "location_color_dict = {}\n",
    "for location in all_locations:\n",
    "    # Generate a random color in hexadecimal format\n",
    "    color = \"#{:06x}\".format(random.randint(0, 0xFFFFFF),seed=100)\n",
    "    location_color_dict[location] = color\n",
    "    \n",
    "print (locations_yearly_counts.sum().nlargest(5).index)\n",
    "\n",
    "top_location_color_dict = {\n",
    "    \"bogota\": '#6b7c93',\n",
    "    \"medellin\": '#ff7f0e',\n",
    "    \"barranquilla\": '#2ca02c',\n",
    "    \"madrid\": '#d62728',\n",
    "    \"barcelona\": '#9467bd'\n",
    "}\n",
    "\n",
    "location_color_dict.update(top_location_color_dict)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0c0dEVFSx6Caiz1qVKlWwZ88e3LhxA7Vq1YKVlRVOnz4NIYRC4R0fH4+MjAz5aI+VlRUyMjLw5MkThSJYCIG4uDh88cUXCsepU6cOvvnmGwQEBAAAfv31V4UC28bGBseOHYNMJsuz8FblmPr6+jkWfgNyFgqFQUdHB0FBQQgKCsLLly+xf/9+jBs3Dn5+frh///57VyCOi4vLte3d4mLIkCFYs2YN/vrrL+zevRsWFhYqraz97pcu7/Pq1Svs2LEDkyZNwpgxY+Ttqamp8utws9nY2ODBgwf59mdtbQ1DQ0OsWLEiz+15yX4tYmNjc2x79OhRvvsq432vjYGBAV69epWjPa8vCvJ6T/X09GBiYqJ0XjY2NsjMzERcXFyeBdnBgwfx6NEjHD58WD66DUDpNQXyYmVllefrDeT/fqli7dq18PX1xa+//qrQ/ubNmwL1l/1Zyes9+FDW1taQJAnh4eG5riGR3XblyhVcunQJYWFh6NGjh3x79noBuclvTQMiok8Zp5cT0WctIiICwH8LRjVq1AgJCQnYtm2bQlz2yr/Z08Kz/1y7dq1C3ObNm5GYmJhj+jgA9OjRA3/++SdWrlyJ7t27K0yzbd68OVJSUvK9D7Yqx3RxccHly5cV4g4ePIiEhIQ8+38ffX39947aWVhYoGPHjhg4cCCeP3+u1Kja+vXrIYSQP7937x5OnDiRYwGpmjVrwtvbGzNnzsS6devg7+8PY2PjgpyKUiRJghAiR2Hx+++/55gi3bx5cxw6dAjXr1/Ps7+WLVvi9u3bsLKygpeXV45HbqOq2erUqQNDQ8Mc7/2DBw9w8ODBXD9vhcnFxQU3btxQ+CLn2bNnCivrv23Lli1ISUmRP3/z5g3+/vtv+Pj4KEyNfp/mzZsDQI6C9G3Zhdq771NusyBUmcHRqFEjREZG4sKFCwrtq1evhiRJaNCgwXv7UIYkSTlyv3z5cq6r9SvD3d0d9vb2ef5efaiWLVtCCIGHDx/m+jn29PQEoNr7QkRU3HGkm4g+G1euXEFGRgaArIJhy5Yt2LdvH9q1awdXV1cAQPfu3fHLL7+gR48euHv3Ljw9PXHs2DH8/PPPaNGiBRo3bgwAaNKkCfz8/DB69Gi8fv0adevWla8kXr16dXTr1i3XHDp27AgjIyN07NgRycnJWL9+PfT09PDdd99h5cqV6NevH65fv44GDRpAJpPh9OnT8PDwwLfffqvSMbt164YJEyZg4sSJqF+/PiIjI7Fo0SKlp/XmxtPTE3/++Sc2bNiAMmXKwMDAAJ6enmjVqpX8Ps82Nja4d+8eQkJC4OzsjHLlyr233/j4eLRr1w59+vTBq1evMGnSJBgYGGDs2LE5YocMGYLOnTtDkiQMGDCgwOeiDDMzM9SrVw+zZ8+GtbU1XFxccOTIESxfvhwWFhYKsVOmTME///yDevXqYdy4cfD09MTLly+xe/duBAUFoUKFChg6dCg2b96MevXqYdiwYahSpQpkMhliYmKwd+9eDB8+HF9++WWuuVhYWGDChAkYN24cunfvju+++w7Pnj3D5MmTYWBgoHDrOXXo1q0bli5diq5du6JPnz549uwZZs2aBTMzs1zjtbW10aRJEwQFBUEmk2HmzJl4/fo1Jk+erNJxfXx80K1bN0ybNg2PHz9Gy5Ytoa+vj4sXL8LIyAiDBw+Gt7c3LC0t0a9fP0yaNAm6urpYt26dwuUJ2bILwpkzZ6J58+bQ1tZGlSpVFKZPZxs2bBhWr16Nr7/+GlOmTIGzszN27tyJxYsXo3///ihfvrxK55KXli1bYurUqZg0aRLq16+P69evY8qUKXB1dZX/faUKLS0tTJ06Fb1795b/Xr18+RLBwcH5TtNXVt26dfHDDz+gZ8+eOHfuHOrVqwdjY2PExsbi2LFj8PT0RP/+/VGhQgWULVsWY8aMgRACJUqUwN9///3Bl0IQEX2SNLWCGxHRx5Lb6uXm5uaiWrVqYt68eSIlJUUh/tmzZ6Jfv37C3t5e6OjoCGdnZzF27NgcccnJyWL06NHC2dlZ6OrqCnt7e9G/f3/x4sULhbjs1cvfdujQIWFiYiKaNWsmkpKS5P1NnDhRlCtXTujp6QkrKyvRsGFDceLECZWPmZqaKkaNGiWcnJyEoaGhqF+/voiIiMhz9fJ3V7rObSXvu3fviqZNmwpTU1MBQL4K8ty5c4W3t7ewtrYWenp6onTp0iIgIEDcvXs33/cl+xhr1qwRgYGBwsbGRujr6wsfHx9x7ty5XPdJTU0V+vr6olmzZvn2/TZlV/PO7ZwfPHggOnToICwtLYWpqalo1qyZuHLlSo7XUYis1Zt79eol7OzshK6urnBwcBCdOnVSWHE9ISFBjB8/Xri7uws9PT1hbm4uPD09xbBhw+Qrhefn999/F1WqVJHv26ZNG3H16tUCna+qsatWrRIeHh7CwMBAVKxYUWzYsCHP1ctnzpwpJk+eLBwdHYWenp6oXr262LNnT67Hzm/1ciGyVuOeP3++qFy5svy869SpI/7++295zIkTJ0SdOnWEkZGRsLGxEb179xYXLlwQAMTKlSvlcampqaJ3797CxsZGSJKkcPzc3tN79+6JLl26CCsrK6Grqyvc3d3F7NmzRWZmZo5znj17do7XDHncReBtqampYsSIEaJUqVLCwMBA1KhRQ2zbti3P11bZ4/z+++/yv0vKly8vVqxYkevrm5/8jrlixQrx5ZdfCmNjY2FoaCjKli0runfvrvC7GxkZKZo0aSJMTU2FpaWl+Oabb0RMTEyOfLNXL89eIZ2IqLiRhHhr7hEREVER9vfff6N169bYuXMnWrRooel0iIiIiN6LRTcRERV5kZGRuHfvHoYMGQJjY2NcuHCBiy4RERHRJ4ELqRERUZE3YMAAtG7dGpaWlli/fj0LbiIiIvpkcKSbiIiIiIiISE040k1ERERERERF0tGjR9GqVSs4ODhAkqQct3XNzZEjR1CzZk0YGBigTJkyWLJkifoTzQeLbiIiIiIiIiqSEhMTUbVqVSxatEip+OjoaLRo0QI+Pj64ePEixo0bh8DAQGzevFnNmeaN08uJiIiIiIioyJMkCVu3bkXbtm3zjBk9ejS2b9+OqKgoeVu/fv1w6dIlnDx58iNkmZOORo5axGRkZODixYuwtbWFlhYH/4mIiIiIiNRBJpMhJiYGFStWhI7Of+Wovr4+9PX1P7j/kydPomnTpgptfn5+WL58OdLT06Grq/vBx1AVi24AFy9eRK1atTSdBhERERER0Wdp0qRJCA4O/uB+4uLiYGtrq9Bma2uLjIwMPH36FPb29h98DFWx6Abkb8qZM2c08iYQERERERF9DmJjY1GrVi1cuXIFTk5O8vbCGOXO9u6tRbOvqNbULUdZdAPyKeX29vZwdHTUcDZERERERETFm7m5OczMzAq9Xzs7O8TFxSm0xcfHQ0dHB1ZWVoV+PGXwAmYiIiIiIiIqFurUqYN9+/YptO3duxdeXl4auZ4bYNFNRERERERERVRCQgIiIiIQEREBIOuWYBEREYiJiQEAjB07Ft27d5fH9+vXD/fu3UNQUBCioqKwYsUKLF++HCNGjNBE+gA4vZyIiIiIiIiKqHPnzqFBgwby50FBQQCAHj16ICwsDLGxsfICHABcXV2xa9cuDBs2DL/88gscHBywcOFCdOjQ4aPnno336Qbw4MEDODk54f79+/le052ZmYn09PSPmBkVRbq6utDW1tZ0GkREREREnxxla6/ihCPdShBCIC4uDi9fvtR0KlREWFhYwM7OTmMrIBIRERER0aeBRbcSsgvukiVLwsjIiIXWZ0wIgaSkJMTHxwMAbzFHRERERET5YtH9HpmZmfKCW1NLzFPRYmhoCCDr1gMlS5bkVHMiIiIiIsoTVy9/j+xruI2MjDScCRUl2Z8HXuNPRERERET5YdGtJE4pp7fx80BERERERMpg0U1ERERERESkJrymuxjz9fVFtWrVEBISoulUiKiQ8RaGRFTU8JaaRES5Y9FNaidJErZu3Yq2bdtqOhWiTx5vYUhERRlvqUlElBOLbiKiTwhvYUhERRFvqUlElDcW3cVcRkYGBg0ahLVr10JbWxv9+/fH1KlTIUkSXrx4gSFDhuDvv/9Gamoq6tevj4ULF6JcuXLy/X/77TdMmTIFz549g5+fH3x8fDBlyhSFUbZff/0Vc+bMwf379+Hq6orx48ejW7duAAAXFxcAQLt27QAAzs7OuHv3Lm7fvo2goCCcOnUKiYmJ8PDwwPTp09G4ceOP9toQfWp4C0MiKsp4S00iotxxITUVCSGQmpaukYcQQuV8V61aBR0dHZw+fRoLFy7E/Pnz8fvvvwMA/P39ce7cOWzfvh0nT56EEAItWrSQXyd6/Phx9OvXD0OGDEFERASaNGmCn376SaH/rVu3YsiQIRg+fDiuXLmCvn37omfPnjh06BAA4OzZswCAlStXIjY2Vv48ISEBLVq0wP79+3Hx4kX4+fmhVatWiImJKfB7Q1Tc8RaGRFTU8ZaaREQ5SaIglVwx8+DBAzg5OeH+/ftwdHRU2JaSkoLo6Gi4urrCwMAAqWnpGD51qUbynDuhL/T1dJWO9/X1RXx8PK5evSqfgjpmzBhs374df/31F8qXL4/jx4/D29sbAPDs2TM4OTlh1apV+Oabb/Dtt98iISEBO3bskPfZtWtX7NixQz7SXbduXVSqVAnLli2Tx3Tq1AmJiYnYuXMnAOWv6a5UqRL69++PQYMGKX2OmvLu54LoY+DnjoiKOv49RUTvk1/tVVxxpLuYq127tsI1n3Xq1MHNmzcRGRkJHR0dfPnll/JtVlZWcHd3R1RUFADg+vXrqFWrlkJ/7z6PiopC3bp1Fdrq1q0r7yMviYmJGDVqFCpWrAgLCwuYmJjg2rVrHOkmIiIiIqJihdd0q0hPVwdzJ/TV2LHVTQghL9Lf/vnt7e/KLeZ9izuNHDkSe/bswZw5c+Dm5gZDQ0N07NgRaWlpH3gGRERAWFgYhg4d+tms8s67RBARERVdHOlWkSRJ0NfT1cijIKsUnzp1KsfzcuXKoWLFisjIyMDp06fl2549e4YbN27Aw8MDAFChQgWcOXNGYf9z584pPPfw8MCxY8cU2k6cOCHvA8i6b2dmZqZCTHh4OPz9/dGuXTt4enrCzs4Od+/eVfn8iEh1mZnA4cPA+vVZf77z61mohBBo3Lgx/Pz8cmxbvHgxzM3Ni+wMlzt37uC7776Dg4MDDAwM4OjoiDZt2uDGjRvyGEmScn38+eefAIDDhw9DkqRci/+0tDRYW1tj2rRpuR5/+vTpsLa25peRREREnzgW3cXc/fv3ERQUhOvXr2P9+vUIDQ3FkCFDUK5cObRp0wZ9+vTBsWPHcOnSJXTt2hWlSpVCmzZtAACDBw/Grl27MG/ePNy8eRNLly7FP//8o1D8jxw5EmFhYViyZAlu3ryJefPmYcuWLRgxYoQ8xsXFBQcOHEBcXBxevHgBAHBzc8OWLVsQERGBS5cuoUuXLpDJZB/3xSH6DG3ZAri4AA0aAF26ZP3p4pLVrg6SJGHlypU4ffo0li79bz2M6OhojB49GgsWLEDp0qUL9ZiFsYBTWloamjRpgtevX2PLli24fv06NmzYgMqVK+PVq1cKsdkLRb79UGbEWU9PD127dkVYWFius4hWrlyJbt26QU9P74PPh4iIiDSHRXcx1717dyQnJ6NWrVoYOHAgBg8ejB9++AFA1n/oatasiZYtW6JOnToQQmDXrl3Q1c1arK1u3bpYsmQJ5s2bh6pVq2L37t0YNmyYwsIobdu2xYIFCzB79mxUqlQJS5cuxcqVK+Hr6yuPmTt3Lvbt2wcnJydUr14dADB//nxYWlrC29sbrVq1gp+fH2rUqPHxXhiiz9CWLUDHjsCDB4rtDx9mtaur8HZycsKCBQswYsQIREdHQwiBgIAANGrUCLVq1UKLFi1gYmICW1tbdOvWDU+fPpXvu3v3bnz11VewsLCAlZUVWrZsidu3b8u33717F5IkYePGjfD19YWBgQHWrl2rcPy7d+9CS0srx0yd0NBQODs751rwRkZG4s6dO1i8eDFq164NZ2dn1K1bFz/99BO++OILhVgLCwvY2dkpPJRdQCogIAC3b9/G0aNHFdrDw8Nx8+ZNBAQE4OzZs2jSpAmsra1hbm6O+vXr48KFC3n2mdvoekREBCRJUphRdOLECdSrVw+GhoZwcnJCYGAgEhMT5dsXL16McuXKwcDAALa2tujYsaNS50RERETvECTu378vAIj79+/n2JacnCwiIyNFcnKyBjIrenr37i2++uorTaehcfxckCZ8yOcuI0MIR0chgNwfkiSEk1NWnLq0adNG1K9fXyxcuFDY2NiIu3fvCmtrazF27FgRFRUlLly4IJo0aSIaNGgg32fTpk1i8+bN4saNG+LixYuiVatWwtPTU2RmZgohhIiOjhYAhIuLi9i8ebO4c+eOePjwoVi5cqUwNzeX99OkSRMxYMAAhXyqV68uJk6cmGuuDx48EFpaWmLOnDkiI58XBYDYunVrntsPHTokAIgXL17kGfPFF1+IHj16KLT5+/uLWrVqCSGEOHDggFizZo2IjIwUkZGRIiAgQNja2orXr1/nmkdux7x48aIAIKKjo4UQQly+fFmYmJiI+fPnixs3bojjx4+L6tWrC39/fyGEEGfPnhXa2trijz/+EHfv3hUXLlwQCxYsyPMciLLx30ciep/8aq/iikW3YNGdn9mzZ4uIiAhx8+ZNsXDhQqGrqyt+++03TaelcZ/754I040M+d4cO5V1wv/04dKjQ05Z7/PixsLGxEVpaWmLLli1iwoQJomnTpgox2X8fX79+Pdc+4uPjBQDx77//CiH+K7pDQkIU4t4tujds2CAsLS1FSkqKEEKIiIgIIUmSvAjNzaJFi4SRkZEwNTUVDRo0EFOmTBG3b99WiAEgDAwMhLGxscIjO06ZovvXX38VxsbG4s2bN0IIId68eSOMjY3F0qVLc43PyMgQpqam4u+//1bIQ5Wiu1u3buKHH35Q6Dc8PFxoaWmJ5ORksXnzZmFmZqZQ2BMpg/8+EtH7fI5FN6eXU77OnDmDJk2awNPTE0uWLMHChQvRu3dvTadFRCqKjS3cuIIoWbIkfvjhB3h4eKBdu3Y4f/48Dh06BBMTE/mjQoUKACCfQn779m106dIFZcqUgZmZGVxdXQEgx+JrXl5e+R67bdu20NHRwdatWwEAK1asQIMGDeDi4pLnPgMHDkRcXBzWrl2LOnXq4P/+7/9QqVIl7Nu3TyFu/vz5iIiIUHg4OTkp/bp89913kMlk2LBhAwBgw4YNEELg22+/BQDEx8ejX79+KF++PMzNzWFubo6EhIQPWoDu/PnzCAsLU3jt/fz8IJPJEB0djSZNmsDZ2RllypRBt27dsG7dOiQlJRX4eERERJ8z3jKM8rVx40ZNp0BEhcDevnDjCkpHRwc6Oln/9MhkMrRq1QozZ87MJY+sRFq1agUnJyf89ttvcHBwgEwmQ+XKlXOs6G1sbJzvcfX09NCtWzesXLkS7du3xx9//IGQkJD35mtqaorWrVujdevWmDZtGvz8/DBt2jQ0adJEHmNnZwc3N7f39pUXc3NzdOzYEStXrkRAQABWrlyJjh07wszMDADg7++PJ0+eICQkBM7OztDX10edOnXyXNVcSyvr+3Tx1rXq7y4uJ5PJ0LdvXwQGBubYv3Tp0tDT08OFCxdw+PBh7N27FxMnTkRwcDDOnj0LCwuLAp8rERHR54hFNxHRZ8DHB3B0zFo0LZd1wyBJWdt9fD5eTjVq1MDmzZvh4uIiL8Tf9uzZM0RFRWHp0qXw+V9i796iUBW9e/dG5cqVsXjxYqSnp6N9+/Yq7S9JEipUqIATJ04UOIe8BAQEwNfXFzt27MDx48fx888/y7eFh4dj8eLFaNGiBYCsu1K8vdjcu2xsbAAAsbGxsLS0BJC1kNrbatSogatXr+b7ZYGOjg4aN26Mxo0bY9KkSbCwsMDBgwdVft2IiIg+d5xeTkT0GdDWBhYsyPr5rbv+KTwPCcmK+1gGDhyI58+f47vvvsOZM2dw584d7N27F7169UJmZiYsLS1hZWWFZcuW4datWzh48CCCgoIKfDwPDw/Url0bo0ePxnfffQdDQ8M8YyMiItCmTRts2rQJkZGRuHXrFpYvX44VK1bIb6uY7eXLl4iLi1N4vL0KuDLq168PNzc3dO/eHW5ubqhXr558m5ubG9asWYOoqCicPn0a33//fb65u7m5wcnJCcHBwbhx4wZ27tyJuXPnKsSMHj0aJ0+exMCBAxEREYGbN29i+/btGDx4MABgx44dWLhwISIiInDv3j2sXr0aMpkM7u7uKp0XERERsegmIvpstG8PbNoElCql2O7omNX+sQcwHRwccPz4cWRmZsLPzw+VK1fGkCFDYG5uDi0tLWhpaeHPP//E+fPnUblyZQwbNgyzZ8/+oGMGBAQgLS0NvXr1yjfO0dERLi4umDx5Mr788kvUqFEDCxYswOTJk/Hjjz8qxPbs2RP29vYKj9DQUJVz69WrF168eJEjtxUrVuDFixeoXr06unXrhsDAQJQsWTLPfnR1dbF+/Xpcu3YNVatWxcyZMzFt2jSFmCpVquDIkSO4efMmfHx8UL16dUyYMEE+rd/CwgJbtmxBw4YN4eHhgSVLlmD9+vWoVKmSyudFRET0uZOEyG2i4eflwYMHcHJywv379+Ho6KiwLSUlBdHR0XB1dVX6vqtU/PFzQZpQWJ+7zEwgPDxr0TR7+6wp5R9zhFuTfvrpJ/z555/4999/NZ0KUbHEfx+J6H3yq72KK17TTUT0mdHWBnx9NZ3Fx5WQkICoqCiEhoZi6tSpmk6HiIiIPiMsuomIqNgbNGgQ1q9fj7Zt2753ajkR0aeoTf1TSsf+daS2GjMhonfxmm76YL6+vhg6dKjS8WFhYQq3nAkODka1atXkz/39/dG2bdtCy4+IKCwsDKmpqdiwYQO0P5e59ERERFQkcKSbipwFCxaASw0QEREREVFxwKKbihxzc3NNp0BERERERFQoWHQXY76+vvD09IS2tjZWrVoFPT09TJ06Fd9//z0GDRqETZs2oWTJkli0aBGaN28OAIiMjMSIESNw9OhRGBsbo2nTppg/fz6sra0BAImJiejfvz+2bNkCU1NTjBgxIsdx09LSMH78eKxbtw4vX75E5cqVMXPmTPgquXKTv78/Xr58iW3btsnPo0qVKjAwMMDvv/8OPT099OvXD8HBwYXxMhERERF98vonrlUhmtd0E31MvKZbVUIAaYmaeRRgyvWqVatgbW2NM2fOYPDgwejfvz+++eYbeHt748KFC/Dz80O3bt2QlJSE2NhY1K9fH9WqVcO5c+ewe/duPH78GJ06dZL3N3LkSBw6dAhbt27F3r17cfjwYZw/f17hmD179sTx48fx559/4vLly/jmm2/QrFkz3Lx5s8Av+6pVq2BsbIzTp09j1qxZmDJlCvbt21fg/oiIiIiIiD4GjnSrKj0J+NlBM8ce9wjQM1Zpl6pVq2L8+PEAgLFjx2LGjBmwtrZGnz59AAATJ07Er7/+isuXL2PXrl2oUaMGfv75Z/n+K1asgJOTE27cuAEHBwcsX74cq1evRpMmTQBkFcNv31/v9u3bWL9+PR48eAAHh6zXacSIEdi9ezdWrlyp0LcqqlSpgkmTJgEAypUrh0WLFuHAgQPyPIiIiIiIiIoiFt3FXJUqVeQ/a2trw8rKCp6envI2W1tbAEB8fDzOnz+PQ4cOwcTEJEc/t2/fRnJyMtLS0lCnTh15e4kSJeDu7i5/fuHCBQghUL58eYX9U1NTYWVlVSjnAQD29vaIj48vcH9EREREREQfA4tuVekaZY04a+rYqu6iq6vwXJIkhTZJkgAAMpkMMpkMrVq1wsyZM3P0Y29vr9T0cJlMBm1tbZw/fz7HbXlyK+aVldt5yGSyAvdHREWHJEnYunUrbxX4kYWFhWHo0KF4+fKlplNRm23btmHEiBGIjo7G4MGDERISoumU1I6/T0RERQ+LblVJkspTvD8VNWrUwObNm+Hi4gIdnZwfDTc3N+jq6uLUqVMoXbo0AODFixe4ceMG6tevDwCoXr06MjMzER8fDx8fn4+aPxEVTe8ujviu2NhYWFpaftykVJD95SQAGBsbo2zZshg2bBj8/f01l1Qh6Ny5M1q0aKGRY2/evBmzZs3CtWvXIJPJULp0aTRr1gxz584FkPWFQM+ePXPsp6+vj5SUFADv/1wBQN++fdGzZ08EBgbC1NS0UM9BmeOrU3BwMLZt24aIiAiF9qL++0Tq4zb/jqZTIKI8cCE1khs4cCCeP3+O7777DmfOnMGdO3ewd+9e9OrVC5mZmTAxMUFAQABGjhyJAwcO4MqVK/D394eW1n8fo/Lly+P7779H9+7dsWXLFkRHR+Ps2bOYOXMmdu3apcGzI6Kiys7ODvr6+hrNQQiBjIyMPLevXLkSsbGxuHTpEjp37oyePXtiz549as0pLS1Nrf0bGhqiZMmSaj1Gbvbv349vv/0WHTt2xJkzZ3D+/Hn89NNPOc7XzMwMsbGxCo979+4pfZyEhATEx8fDz88PDg4OBS661f0+FLai8PtERESKWHSTnIODA44fP47MzEz4+fmhcuXKGDJkCMzNzeWF9ezZs1GvXj20bt0ajRs3xldffYWaNWsq9LNy5Up0794dw4cPh7u7O1q3bo3Tp0/DyclJE6dFREWcJEny0cK7d+9CkiRs2bIFDRo0gJGREapWrYqTJ08q7HPixAnUq1cPhoaGcHJyQmBgIBITE+Xb165dCy8vL5iamsLOzg5dunRRWAfi8OHDkCQJe/bsgZeXF/T19REeHp5njhYWFrCzs0PZsmUxbtw4lChRAnv37pVvf/XqFX744QeULFkSZmZmaNiwIS5duqTQx7Rp01CyZEmYmpqid+/eGDNmDKpVqybf7u/vj7Zt22L69OlwcHCQr43x8OFDdO7cGZaWlrCyskKbNm1w9+5dhXOpVasWjI2NYWFhgbp168qL00uXLqFBgwYwNTWFmZkZatasiXPnzgHIGk22sLBQyPHXX39F2bJloaenB3d3d6xZsybHe/X777+jXbt2MDIyQrly5bB9+/Y8X7fc7NixA1999RVGjhwJd3d3lC9fHm3btkVoaGiOY9nZ2Sk8stcheZ/Dhw/Li+yGDRtCkiQcPnwYQNYoe6VKlaCvrw8XFxf56Ho2FxcXTJs2Df7+/jA3N5cvPKqqI0eOoFatWtDX14e9vT3GjBmj8MWOTCbDzJkz4ebmBn19fZQuXRo//fSTfPvo0aNRvnx5GBkZoUyZMpgwYQLS09MBZL13kydPxqVLlyBJEiRJQlhYmPx1e3v0/d9//0XDhg1haGgIKysr/PDDD0hISJBvz/7czZkzB/b29rCyssLAgQPlxwKAxYsXo1y5cjAwMICtrS06duxYoNeEiOizJUjcv39fABD379/PsS05OVlERkaK5ORkDWRGRRU/F6QJeX7uEhLyfqgSm5SkXKyKevToIdq0aZPndgBi69atQgghoqOjBQBRoUIFsWPHDnH9+nXRsWNH4ezsLNLT04UQQly+fFmYmJiI+fPnixs3bojjx4+L6tWrC39/f3mfy5cvF7t27RK3b98WJ0+eFLVr1xbNmzeXbz906JAAIKpUqSL27t0rbt26JZ4+ffre/DIyMsSGDRsEADF69GghhBAymUzUrVtXtGrVSpw9e1bcuHFDDB8+XFhZWYlnz54JIYRYu3atMDAwECtWrBDXr18XkydPFmZmZqJq1aoKr5OJiYno1q2buHLlivj3339FYmKiKFeunOjVq5e4fPmyiIyMFF26dBHu7u4iNTVVpKenC3NzczFixAhx69YtERkZKcLCwsS9e/eEEEJUqlRJdO3aVURFRYkbN26IjRs3ioiICCGEECtXrhTm5uby42/ZskXo6uqKX375RVy/fl3MnTtXaGtri4MHDyq8Fo6OjuKPP/4QN2/eFIGBgcLExER+nkII4ezsLCZNmpTn+z19+nRhY2Mj/v333zxj3s0tN/l9rlJTU8X169cFALF582YRGxsrUlNTxblz54SWlpaYMmWKuH79uli5cqUwNDQUK1euVMjfzMxMzJ49W9y8eVPcvHlT5eM/ePBAGBkZiQEDBoioqCixdetWYW1trfC6jBo1SlhaWoqwsDBx69YtER4eLn777Tf59qlTp4rjx4+L6OhosX37dmFraytmzpwphBAiKSlJDB8+XFSqVEnExsaK2NhYkfS/39+3P6+JiYnCwcFBtG/fXvz777/iwIEDwtXVVfTo0UPhPMzMzES/fv1EVFSU+Pvvv4WRkZFYtmyZEEKIs2fPCm1tbfHHH3+Iu3fvigsXLogFCxbk+b7w30fNuXm0udIPIk3Kr/Yqrlh0CxbdpDp+LkgT8vzcAXk/WrRQjDUyyju2fn3FWGvr3ONUVJCi+/fff5dvv3r1qgAgoqKihBBCdOvWTfzwww8KfYSHhwstLa08fyfPnDkjAIg3b94IIf4rurdt2/be/AEIAwMDYWxsLLS1tQUAUaJECXkxduDAAWFmZiZSUlIU9itbtqxYunSpEEKIL7/8UgwcOFBhe926dXMU3ba2tiI1NVXetnz5cuHu7i5kMpm8LTU1VRgaGoo9e/aIZ8+eCQDi8OHDueZuamoqwsLCct32bmHr7e0t+vTpoxDzzTffiBZvfYYAiPHjx8ufJyQkCEmSxD///CNva9iwoQgNDc31mNn7tGjRQgAQzs7OonPnzmL58uUKr9/KlSsFAGFsbKzwaNKkiTzmfZ+rFy9eCADi0KFD8rYuXboo9CGEECNHjhQVK1aUP3d2dhZt27bNs19ljj9u3Lgc79svv/wiTExMRGZmpnj9+rXQ19dXKLLfZ9asWaJmzZry55MmTVL4/GR7+/dp2bJlwtLSUiS89WXZzp07hZaWloiLi5Ofh7Ozs8jIyJDHfPPNN6Jz585CCCE2b94szMzMxOvXr5XKk/8+as7ByrWVfhBp0udYdHN6ORERFTlv3ybQ3t4eAOTTw8+fP4+wsDCYmJjIH35+fpDJZIiOjgYAXLx4EW3atIGzszNMTU3h6+sLAIiJiVE4jpeXl1L5zJ8/HxEREdi3bx+qVauG+fPnw83NTZ5PQkICrKysFHKKjo7G7du3AQDXr19HrVq1FPp89zkAeHp6Qk9PT/78/PnzuHXrFkxNTeX9lihRAikpKbh9+zZKlCgBf39/+Pn5oVWrVliwYAFiY2Pl+wcFBaF3795o3LgxZsyYIc8nN1FRUahbt65CW926dREVFaXQ9vZ7Y2xsDFNTU4Wp+wcOHMCgQYPyPI6xsTF27tyJW7duYfz48TAxMcHw4cNRq1YtJCUlyeNMTU0RERGh8Fi5cmWe/Sojr3O8efMmMjMz5W3Kfi7yO06dOnUUFuGrW7cuEhIS8ODBA0RFRSE1NRWNGjXKs49Nmzbhq6++gp2dHUxMTDBhwoQcn19l8qhatSqMjf9bALZu3bqQyWS4fv26vK1SpUoKdxx5+7acTZo0gbOzM8qUKYNu3bph3bp1Cu8TERG9H1cvJyL61L11fWYO79y6D/nd317rne9h37pu+GPL69aG2X/27dsXgYGBOfYrXbo0EhMT0bRpUzRt2hRr166FjY0NYmJi4Ofnl2NRrLeLkfzY2dnBzc0Nbm5u+L//+z9Ur14dXl5eqFixImQyGezt7eXXDL/t7Wum3y7AgKzF2971bj4ymQw1a9bEunXrcsTa2NgAyFpHIzAwELt378aGDRswfvx47Nu3D7Vr10ZwcDC6dOmCnTt34p9//sGkSZPw559/ol27drmeZ245vttWWLdwLFu2LMqWLYvevXvjxx9/RPny5bFhwwb5quVaWlryLzYKS27no8z7UJjHkSQJhoaG+e5/6tQpfPvtt5g8eTL8/Pxgbm6OP//8M8f15wXJI9vb7fm9p6amprhw4QIOHz6MvXv3YuLEiQgODsbZs2dzrAlARES540g3EdGnztg474eBgfKx7xYCecVpWI0aNXD16lV5Efz2Q09PD9euXcPTp08xY8YM+Pj4oEKFCgojsR/Kzc0NHTp0wNixY+X5xMXFQUdHJ0c+1tbWAAB3d3ecOXNGoZ/sBc3ed643b95EyZIlc/Rtbm4uj6tevTrGjh2LEydOoHLlyvjjjz/k28qXL49hw4Zh7969aN++fZ6jxR4eHjh27JhC24kTJ+Dh4aHcC/MBXFxcYGRkpLAYnjpUrFgx13MsX768wkhvYRznxIkTCgX9iRMnYGpqilKlSqFcuXIwNDTEgQMHct3/+PHjcHZ2xo8//ggvLy+UK1cux8rtenp6CqPzeeURERGh8LoeP34cWlpa8oX6lKGjo4PGjRtj1qxZuHz5Mu7evYuDBw8qvT8R0eeOI91ERKR2r169ynE/4RIlSqB06dIq9zV69GjUrl0bAwcORJ8+fWBsbIyoqCjs27cPoaGhKF26NPT09BAaGop+/frhypUrmDp1aiGdSZbhw4ejatWqOHfuHBo3bow6deqgbdu2mDlzJtzd3fHo0SPs2rULbdu2hZeXFwYPHow+ffrAy8sL3t7e2LBhAy5fvowyZcrke5zvv/8es2fPRps2bTBlyhQ4OjoiJiYGW7ZswciRI5Geno5ly5ahdevWcHBwwPXr13Hjxg10794dycnJGDlyJDp27AhXV1c8ePAAZ8+eRYcOHXI91siRI9GpUyfUqFEDjRo1wt9//40tW7Zg//79Kr02jRo1Qrt27fKcYh4cHIykpCS0aNECzs7OePnyJRYuXIj09HQ0adJEHieEQFxcXI79S5YsqXCrSlUMHz4cX3zxBaZOnYrOnTvj5MmTWLRoERYvXlyg/vL6XA8YMAAhISEYPHgwBg0ahOvXr2PSpEkICgqClpYWDAwMMHr0aIwaNQp6enqoW7cunjx5gqtXryIgIABubm6IiYnBn3/+iS+++AI7d+7E1q1bFY7j4uKC6OhoREREwNHREaampjluFfb9999j0qRJ6NGjB4KDg/HkyRMMHjwY3bp1U3ol+B07duDOnTuoV68eLC0tsWvXLshkMri7uxfoNSP1adAhUtMpEFEeWHQTEZHaHT58GNWrV1do69Gjh/w2R6qoUqUKjhw5gh9//BE+Pj4QQqBs2bLo3LkzgKxp12FhYRg3bhwWLlyIGjVqYM6cOWjdunVhnAqArGuvGzdujIkTJ2LXrl3YtWsXfvzxR/Tq1QtPnjyBnZ0d6tWrJy9svv/+e9y5cwcjRoxASkoKOnXqBH9//xyj3+8yMjLC0aNHMXr0aLRv3x5v3rxBqVKl0KhRI5iZmSE5ORnXrl3DqlWr8OzZM9jb22PQoEHo27cvMjIy8OzZM3Tv3h2PHz+GtbU12rdvj8mTJ+d6rLZt22LBggWYPXs2AgMD4erqipUrV8qvh1fW7du38fTp0zy3169fH7/88os8L0tLS1SvXh179+5VKORev34tv57/bbGxsbCzs1Mpp2w1atTAxo0bMXHiREydOhX29vaYMmUK/P39C9Rffp/rXbt2YeTIkahatSpKlCiBgIAAjB8/Xh43YcIE6OjoYOLEiXj06BHs7e3Rr18/AECbNm0wbNgwDBo0CKmpqfj6668xYcIEBAcHy/fv0KGD/NZ6L1++xMqVK3Och5GREfbs2YMhQ4bgiy++gJGRETp06IB58+YpfY4WFhbYsmULgoODkZKSgnLlymH9+vWoVKmS6i8YEdFnShK5Xcz0mXnw4AGcnJxw//59ODo6KmxLSUlBdHQ0XF1dYfDuNE36bPFzQZrAz13x0qRJE9jZ2eW4FzbRp4x/T2lQsPn7Y+Sxr9SXB9F75Fd7FVcc6SYiIlKzpKQkLFmyBH5+ftDW1sb69euxf/9+7Nu3T9OpERERkZqx6CYiIlIzSZKwa9cuTJs2DampqXB3d8fmzZvRuHFjTadGREREasbVy4sxX19fDB06VNNpaIQkSdi2bRsA4O7du5AkSb7YzeHDhyFJEl6+fKmx/Ijo82JoaIj9+/fj+fPnSExMxIULF9C+fXtNp0VEREQfAUe6qViKjY2FpaWlptMgIiIiIqLPHEe6SWlCCGRkZORoT0tL00A2+bOzs8tx6xSi4oLrXxJRUcW/n4iIcmLRXcxlZGRg0KBBsLCwgJWVFcaPHy//B3Ht2rXw8vKCqakp7Ozs0KVLF8THx8v3zZ6GvWfPHnh5eUFfXx/h4eHw9fXFoEGDEBQUBGtra/l9VefNmwdPT08YGxvDyckJAwYMQEJCgry/sLAwWFhYYM+ePfDw8ICJiQmaNWuG2NhYeUxmZiaCgoLk+Y4aNQo9evRA27Zt5TEuLi4ICQlROM9q1aop3Erl7enl7/Ps2TN89913cHR0hJGRETw9PbF+/XolX2Gij0dXVxdA1qJcRERFUfbfT9l/XxEREaeXq0wIgeSMZI0c21DHEJIkqbTPqlWrEBAQgNOnT+PcuXP44Ycf4OzsjD59+iAtLQ1Tp06Fu7s74uPjMWzYMPj7+2PXrl0KfYwaNQpz5sxBmTJlYGFhIe+3f//+OH78uLyI19LSwsKFC+Hi4oLo6GgMGDAAo0aNwuLFi+V9JSUlYc6cOVizZg20tLTQtWtXjBgxAuvWrQMAzJ07FytWrMDy5ctRsWJFzJ07F1u3bkXDhg0/4JXLX0pKCmrWrInRo0fDzMwMO3fuRLdu3VCmTBl8+eWXajsukaq0tbVhYWEh/3LMyMhI5b8TiIjUQQiBpKQkxMfHw8LCAtra2ppOiYioyGDRraLkjGR8+YdmCrHTXU7DSNdIpX2cnJwwf/58SJIEd3d3/Pvvv5g/fz769OmDXr16yePKlCmDhQsXolatWkhISICJiYl825QpU+Sj2dnc3Nwwa9Yshba3F21zdXXF1KlT0b9/f4WiOz09HUuWLEHZsmUBAIMGDcKUKVPk20NCQjB27Fh06NABALBkyRLs2bNHpXNWValSpTBixAj588GDB2P37t34v//7PxbdVOTY2dkBgMKsFCKiosLCwkL+9xQREWVh0V3M1a5dW2EkrE6dOpg7dy4yMzNx+fJlBAcHIyIiAs+fP4dMJgMAxMTEoGLFivJ9vLy8cvSbW9uhQ4fw888/IzIyEq9fv0ZGRgZSUlKQmJgIY2NjAFkjc9kFNwDY29vLi4dXr14hNjYWderUkW/X0dGBl5eXWq8Ry8zMxIwZM7BhwwY8fPgQqampSE1NledMVJRIkgR7e3uULFkS6enpmk6HiEhOV1eXI9xERLlg0a0iQx1DnO5yWmPHLiwpKSlo2rQpmjZtirVr18LGxgYxMTHw8/PLsTBabsXnu2337t1DixYt0K9fP0ydOhUlSpTAsWPHEBAQoFAYvHuNlyRJKhfUWlpaOfb5kOJj7ty5mD9/PkJCQuTXpA8dOrRILhBHlE1bW5v/uSUiIiL6BLDoVpEkSSpP8dakU6dO5Xherlw5XLt2DU+fPsWMGTPg5OQEADh37lyBj3Pu3DlkZGRg7ty50NLKWp9v48aNKvVhbm4Oe3t7nDp1CvXq1QOQtRDc+fPnUaNGDXmcjY2NwuJrr1+/RnR0dIFzDw8PR5s2bdC1a1cAgEwmw82bN+Hh4VHgPomIiIiIiACuXl7s3b9/H0FBQbh+/TrWr1+P0NBQDBkyBKVLl4aenh5CQ0Nx584dbN++HVOnTi3wccqWLYuMjAx5f2vWrMGSJUtU7mfIkCGYMWMGtm7dimvXrmHAgAF4+fKlQkzDhg2xZs0ahIeH48qVK+jRo8cHjfi5ublh3759OHHiBKKiotC3b1/ExcUVuD8iIiIiIqJsLLqLue7duyM5ORm1atXCwIEDMXjwYPzwww+wsbFBWFgY/u///g8VK1bEjBkzMGfOnAIfp1q1apg3bx5mzpyJypUrY926dZg+fbrK/QwfPhzdu3eHv78/6tSpA1NTU7Rr104hZuzYsahXrx5atmyJFi1aoG3btgrXiatqwoQJqFGjBvz8/ODr6ws7OzuFW5QREREREREVlCTUuULVJ+LBgwdwcnLC/fv34ejoqLAtJSUF0dHRcHV1hYGBgYYy/Lz5+/vj5cuXSt93+2Pg54KIiIiKlGBzFWJfqS8PovfIr/YqrjjSTURERERERKQmLLqJiIiIiIiI1ISrl1ORFxYWpukUiIiIiIiICoQj3URERERERERqwqKbiIiIiIiISE1YdBMRERERERGpCYtuIiIiIiIiIjVh0U1ERERERESkJiy6iYiIiIiIiNREo0X30aNH0apVKzg4OECSJGzbti3P2L59+0KSJISEhCi0p6amYvDgwbC2toaxsTFat26NBw8eqDdxUuDv74+2bdu+N+597/Hdu3chSRIiIiIKLTciIiIiIiJN0mjRnZiYiKpVq2LRokX5xm3btg2nT5+Gg4NDjm1Dhw7F1q1b8eeff+LYsWNISEhAy5YtkZmZqa60qYBiY2PRvHlzTadBRERERET00eho8uDNmzd/bxH28OFDDBo0CHv27MHXX3+tsO3Vq1dYvnw51qxZg8aNGwMA1q5dCycnJ+zfvx9+fn5qy52Ul5aWBj09PdjZ2Wk6FSIiIiIioo+qSF/TLZPJ0K1bN4wcORKVKlXKsf38+fNIT09H06ZN5W0ODg6oXLkyTpw4kWe/qampeP36tfzx5s0bteSvab6+vhg8eDCGDh0KS0tL2NraYtmyZUhMTETPnj1hamqKsmXL4p9//gEAZGZmIiAgAK6urjA0NIS7uzsWLFig0GdmZiaCgoJgYWEBKysrjBo1CkKIHMcdNGgQgoKCYG1tjSZNmgDIOb38zJkzqF69OgwMDODl5YWLFy+q9wUhIiIiIiL6yIp00T1z5kzo6OggMDAw1+1xcXHQ09ODpaWlQrutrS3i4uLy7Hf69OkwNzeXPypWrKh0TkIIyJKSNPJ4t7hVxqpVq2BtbY0zZ85g8ODB6N+/P7755ht4e3vjwoUL8PPzQ7du3ZCUlASZTAZHR0ds3LgRkZGRmDhxIsaNG4eNGzfK+5s7dy5WrFiB5cuX49ixY3j+/Dm2bt2a63F1dHRw/PhxLF26NMf2xMREtGzZEu7u7jh//jyCg4MxYsQIlc+PiIiIiIioKNPo9PL8nD9/HgsWLMCFCxcgSZJK+woh8t1n7NixCAoKkj9/+PCh0oW3SE7G9Ro1VcqnsLhfOA/JyEilfapWrYrx48cDyDrvGTNmwNraGn369AEATJw4Eb/++isuX76M2rVrY/LkyfJ9XV1dceLECWzcuBGdOnUCAISEhGDs2LHo0KEDAGDJkiXYs2dPjuO6ublh1qxZeea1bt06ZGZmYsWKFTAyMkKlSpXw4MED9O/fX6XzIyIiIiIiKsqK7Eh3eHg44uPjUbp0aejo6EBHRwf37t3D8OHD4eLiAgCws7NDWloaXrx4obBvfHw8bG1t8+xbX18fZmZm8oepqak6T0WjqlSpIv9ZW1sbVlZW8PT0lLdlv07x8fEAsopoLy8v2NjYwMTEBL/99htiYmIAZF1DHxsbizp16sj319HRgZeXV47j5tb2tqioKFStWhVGb32J8Ha/RERERERExUGRHenu1q2bfHG0bNlToXv27AkAqFmzJnR1dbFv3z75SGxsbCyuXLmS7yjrh5AMDeF+4bxa+lbm2KrS1dVV7EOSFNqyZwTIZDJs3LgRw4YNw9y5c1GnTh2Ymppi9uzZOH36tMrHNTY2znd7QabKExERERERfWo0WnQnJCTg1q1b8ufR0dGIiIhAiRIlULp0aVhZWSnE6+rqws7ODu7u7gAAc3NzBAQEYPjw4bCyskKJEiUwYsQIeHp65ijYC4skSSpP8f5UhIeHw9vbGwMGDJC33b59W/6zubk57O3tcerUKdSrVw8AkJGRgfPnz6NGjRoqHatixYpYs2YNkpOTYfi/LxNOnTpVCGdBRERERERUdGh0evm5c+dQvXp1VK9eHQAQFBSE6tWrY+LEiUr3MX/+fLRt2xadOnVC3bp1YWRkhL///hva2trqSrvYcnNzw7lz57Bnzx7cuHEDEyZMwNmzZxVihgwZghkzZmDr1q24du0aBgwYgJcvX6p8rC5dukBLSwsBAQGIjIzErl27MGfOnEI6EyIiIiIioqJBoyPdvr6+Kk0zvnv3bo42AwMDhIaGIjQ0tBAz+zz169cPERER6Ny5MyRJwnfffYcBAwbIbykGAMOHD0dsbCz8/f2hpaWFXr16oV27dnj16pVKxzIxMcHff/+Nfv36oXr16qhYsSJmzpwpX6CNiIiIiIioOJAEL67FgwcP4OTkhPv378PR0VFhW0pKCqKjo+Hq6goDAwMNZUhFDT8XREREVKQEm6sQq9pgCVFhyq/2Kq6K7OrlRERERERERJ86Ft1EREREREREasKim4iIiIiIiEhNWHQTERERERERqQmLbiIiIiIiIiI10egtw4iIiEizdnsNUjq22blFasyEiIioeOJINxEREREREZGasOgmIiIiIiIiUhMW3URERERERERqwqK7GPP19cXQoUM1nUYO/v7+aNu2rabTICIiIiIiUjsupEZERPQZq7V2rQrRXEiNiIhIVRzpJqUJIZCRkaHpNIiIiIiIiD4ZLLqLuYyMDAwaNAgWFhawsrLC+PHjIYQAAKxduxZeXl4wNTWFnZ0dunTpgvj4ePm+hw8fhiRJ2LNnD7y8vKCvr4/w8HDIZDLMnDkTbm5u0NfXR+nSpfHTTz/J93v48CE6d+4MS0tLWFlZoU2bNrh7926eOaampiIwMBAlS5aEgYEBvvrqK5w9ezZHHgcOHICXlxeMjIzg7e2N69evy2Nu376NNm3awNbWFiYmJvjiiy+wf//+QnwliYiIiIiIVMeiW0VCCKSnZmrkkV0sq2LVqlXQ0dHB6dOnsXDhQsyfPx+///47ACAtLQ1Tp07FpUuXsG3bNkRHR8Pf3z9HH6NGjcL06dMRFRWFKlWqYOzYsZg5cyYmTJiAyMhI/PHHH7C1tQUAJCUloUGDBjAxMcHRo0dx7NgxmJiYoFmzZkhLS8s1x1GjRmHz5s1YtWoVLly4ADc3N/j5+eH58+cKcT/++CPmzp2Lc+fOQUdHB7169ZJvS0hIQIsWLbB//35cvHgRfn5+aNWqFWJiYlR+zYiIiIiIiAqLJApSyRUzDx48gJOTE+7fvw9HR0eFbSkpKYiOjoarqysMDAyQnpqJZUOOaCTPHxbUh66+ttLxvr6+iI+Px9WrVyFJEgBgzJgx2L59OyIjI3PEnz17FrVq1cKbN29gYmKCw4cPo0GDBti2bRvatGkDAHjz5g1sbGywaNEi9O7dO0cfK1aswKxZsxAVFSU/ZlpaGiwsLLBt2zY0bdoU/v7+ePnyJbZt24bExERYWloiLCwMXbp0AQCkp6fDxcUFQ4cOxciRI+V57N+/H40aNQIA7Nq1C19//TWSk5NhYGCQ6/lXqlQJ/fv3x6BBg5R+zZT17ueCiOhTZdG9u9KxL1evVmMmRPRBgs1ViH2lvjyKgJEVflY6dva1cWrMhHKTX+1VXHGku5irXbu2vPgFgDp16uDmzZvIzMzExYsX0aZNGzg7O8PU1BS+vr4AkGN02MvLS/5zVFQUUlNT5cXvu86fP49bt27B1NQUJiYmMDExQYkSJZCSkoLbt2/niL99+zbS09NRt25deZuuri5q1aqFqKgohdgqVarIf7a3twcA+XT4xMREjBo1ChUrVoSFhQVMTExw7do1jnQTEREREZFGcfVyFenoaeGHBfU1duzCkpKSgqZNm6Jp06ZYu3YtbGxsEBMTAz8/vxzTwI2NjeU/Gxoa5tuvTCZDzZo1sW7duhzbbGxscrRlT7R4+4uB7PZ323R1deU/Z2+TyWQAgJEjR2LPnj2YM2cO3NzcYGhoiI4dO+Y5pZ2IiIiIiOhjYNGtIkmSVJrirWmnTp3K8bxcuXK4du0anj59ihkzZsDJyQkAcO7cuff2V65cORgaGuLAgQO5Ti+vUaMGNmzYgJIlS8LMzOy9/bm5uUFPTw/Hjh1TmF5+7tw5le4xHh4eDn9/f7Rr1w5A1jXe+S3eRkRERERE9DGw6C7m7t+/j6CgIPTt2xcXLlxAaGgo5s6di9KlS0NPTw+hoaHo168frly5gqlTp763PwMDA4wePRqjRo2Cnp4e6tatiydPnuDq1asICAjA999/j9mzZ6NNmzaYMmUKHB0dERMTgy1btmDkyJE5rtswNjZG//79MXLkSJQoUQKlS5fGrFmzkJSUhICAAKXP083NDVu2bEGrVq0gSRImTJggHwUnIiIios/H+NASmk6BSAGL7mKue/fuSE5ORq1ataCtrY3Bgwfjhx9+gCRJCAsLw7hx47Bw4ULUqFEDc+bMQevWrd/b54QJE6Cjo4OJEyfi0aNHsLe3R79+/QAARkZGOHr0KEaPHo327dvjzZs3KFWqFBo1apTnyPeMGTMgk8nQrVs3vHnzBl5eXtizZw8sLS2VPs/58+ejV69e8Pb2hrW1NUaPHo3Xr18rvT8REREREZE6cPVyqLZ6ORHAzwURFR9cvZyomODq5XKv9i1ROta8ST81ZkK54erlRERERERERFRoWHQTERERERERqQmv6SYiIvqMbb14U9MpEBEVKiePv5SOfQ1OLyf140g3ERERERERkZqw6FYS15ujt/HzQEREREREymDR/R66uroAgKSkJA1nQkVJ9uch+/NBRERERESUG17T/R7a2tqwsLBAfHw8gKz7UEuSpOGsSFOEEEhKSkJ8fDwsLCygra2t6ZSIiIiI6C2vfz+hfHCw2tIgkmPRrQQ7OzsAkBfeRBYWFvLPBRERERERUV5YdCtBkiTY29ujZMmSSE9P13Q6pGG6uroc4SYiIiIiIqWw6FaBtrY2iy0iIiIiIiJSGotuIiKiz9j99Bi19NsptZPSsRv1N6olByIioqKAq5cTERERERERqQmLbiIiIiIiIiI1YdFNREREREREpCYsuomIiIiIiIjUhAupkUbNuKandOyYCmlqzISI6PN09cwOtfTba+xp5YPnqSUFIiKiIoEj3URERERERERqwqKbiIiIiIiISE1YdBMRERERERGpCa/pJo3afqiG0rFjKqgxESKiz9TMeb7KBwe/UlseRERExRVHuomIiIiIiIjUhEU3ERERERERkZqw6CYiIiIiIiJSE17TTRplaz9X0ykQEZEaBJxIVTr2oRrzICIi0jSOdBMREREREVGRtXjxYri6usLAwAA1a9ZEeHh4vvHr1q1D1apVYWRkBHt7e/Ts2RPPnj37SNnmxKKbiIiIiIiIiqQNGzZg6NCh+PHHH3Hx4kX4+PigefPmiImJyTX+2LFj6N69OwICAnD16lX83//9H86ePYvevXt/5Mz/w6KbiIiIiIiIiqR58+YhICAAvXv3hoeHB0JCQuDk5IRff/011/hTp07BxcUFgYGBcHV1xVdffYW+ffvi3LlzHznz/7DoJiIiIiIioo/qzZs3eP36tfyRmppzLZC0tDScP38eTZs2VWhv2rQpTpw4kWu/3t7eePDgAXbt2gUhBB4/foxNmzbh66+/Vst5KIMLqZFGpbpOUCH6oNKRnierKR37b50IFXIgIiIiIqIPVbFiRYXnkyZNQnBwsELb06dPkZmZCVtbW4V2W1tbxMXF5dqvt7c31q1bh86dOyMlJQUZGRlo3bo1QkNDCzV/VXCkm4iIiIiIiD6qyMhIvHr1Sv4YO3ZsnrGSJCk8F0LkaHu738DAQEycOBHnz5/H7t27ER0djX79+hVq/qrgSDcRERERERF9VKampjAzM8s3xtraGtra2jlGtePj43OMfmebPn066tati5EjRwIAqlSpAmNjY/j4+GDatGmwt7cvnBNQAUe6iYiIiIiIqMjR09NDzZo1sW/fPoX2ffv2wdvbO9d9kpKSoKWlWOZqa2sDyBoh1wSOdJNGhY0bpnzwTvXlQURERERERU9QUBC6desGLy8v1KlTB8uWLUNMTIx8uvjYsWPx8OFDrF69GgDQqlUr9OnTB7/++iv8/PwQGxuLoUOHolatWnBwcNDIObDoJiIiIiIioiKpc+fOePbsGaZMmYLY2FhUrlwZu3btgrOzMwAgNjZW4Z7d/v7+ePPmDRYtWoThw4fDwsICDRs2xMyZMzV1Ciy6iYiIiIiIqOgaMGAABgwYkOu2sLCwHG2DBw/G4MGD1ZyV8nhNNxEREREREZGacKSbiqUjlnc1nQIR0Wct/XiqplMgIiIqEjjSTURERERERKQmLLqJiIiIiIiI1IRFNxEREREREZGasOgmIiIiIiIiUhMupEYaVXl7d6Vj4/FC6diuo9OVjt31l9KhRESkpPipMuWDg9WWBhERkcZxpJuIiIiIiIhITVh0ExEREREREakJi24iIiIiIiIiNeE13aRR6rrm7//ad1U5FyIiIiIiosLGkW4iIiIiIiIiNWHRTURERERERKQmLLqJiIiIiIiI1ITXdFOxZBz9pwrRS9WWBxERERERfd440k1ERERERESkJiy6iYiIiIiIiNSERTcRERERERGRmmi06D569ChatWoFBwcHSJKEbdu2ybelp6dj9OjR8PT0hLGxMRwcHNC9e3c8evRIoY/U1FQMHjwY1tbWMDY2RuvWrfHgwYOPfCZEREREREREOWm06E5MTETVqlWxaNGiHNuSkpJw4cIFTJgwARcuXMCWLVtw48YNtG7dWiFu6NCh2Lp1K/78808cO3YMCQkJaNmyJTIzMz/WaRARERERERHlSqOrlzdv3hzNmzfPdZu5uTn27dun0BYaGopatWohJiYGpUuXxqtXr7B8+XKsWbMGjRs3BgCsXbsWTk5O2L9/P/z8/NR+DkRERERERER5+aSu6X716hUkSYKFhQUA4Pz580hPT0fTpk3lMQ4ODqhcuTJOnDihoSyJiIiIiIiIsnwy9+lOSUnBmDFj0KVLF5iZmQEA4uLioKenB0tLS4VYW1tbxMXF5dlXamoqUlNT5c/fvHmjnqSJiIiIiIjos/ZJjHSnp6fj22+/hUwmw+LFi98bL4SAJEl5bp8+fTrMzc3lj4oVKxZmukREREREREQAPoGiOz09HZ06dUJ0dDT27dsnH+UGADs7O6SlpeHFixcK+8THx8PW1jbPPseOHYtXr17JH5GRkWrLn4iIiIiIiD5fRbrozi64b968if3798PKykphe82aNaGrq6uw4FpsbCyuXLkCb2/vPPvV19eHmZmZ/GFqaqq2cyAiIiIiIqLPl0av6U5ISMCtW7fkz6OjoxEREYESJUrAwcEBHTt2xIULF7Bjxw5kZmbKr9MuUaIE9PT0YG5ujoCAAAwfPhxWVlYoUaIERowYAU9PT/lq5kRERERERESaotGi+9y5c2jQoIH8eVBQEACgR48eCA4Oxvbt2wEA1apVU9jv0KFD8PX1BQDMnz8fOjo66NSpE5KTk9GoUSOEhYVBW1v7o5wDERERERERUV40WnT7+vpCCJHn9vy2ZTMwMEBoaChCQ0MLMzUiIiIiIiKiD1akr+kmIiIiIiIi+pSx6CYiIiIiIiJSE41OLyciIiIiog+3MHS+0rGBwerLg4hy4kg3ERERERERkZqw6CYiIiIiIiJSExbdRERERERERGrCopuIiIiIiIhITbiQGhERfbBzq7cpHevVva3a8iAiIiIqajjSTURERERERKQmLLqJiIiIiIiI1IRFNxEREREREZGa8JpuIhXs9hqkdGyzc4vUmAlR0dLi+55Kx8ajrfoSISL6TDWxma1CdC+15UFEOXGkm4iIiIiIiEhNWHQTERERERERqQmLbiIiIiIiIiI14TXdRCqotXatCtG8ppuIiIiI6HPHkW4iIiIiIiIiNWHRTURERERERKQmLLqJiIiIiIiI1IRFNxEREREREZGacCE1IhV0Ta2hdOwuNeZBVNTET5UpHxystjSIiIiwMHS+0rGBwerLgygbR7qJiIiIiIiI1IRFNxEREREREZGasOgmIiIiIiIiUhNe002kgm87X1c++Jr68iAiosJ1fMCvSsfWXdxfjZkQEVFxw5FuIiIiIiIiIjVh0U1ERERERESkJiy6iYiIiIiIiNSE13QTqWDE1SSlY7urMQ8iIipcHoFjVYjmNd1ERKQ8jnQTERERERERqQmLbiIiIiIiIiI1YdFNREREREREpCYFuqZbJpPh1q1biI+Ph0wmU9hWr169QkmMiIiIiIiI6FOnctF96tQpdOnSBffu3YMQQmGbJEnIzMwstOSIiIiIPoYyP7dWOvblajUmQlRAteKuKx37Ro15FAVNbGarEN1LbXkQZVO56O7Xrx+8vLywc+dO2NvbQ5IkdeRFRERERERE9MlTuei+efMmNm3aBDc3N3XkQ0RERERERFRsqLyQ2pdffolbt26pIxciIiIiIiKiYkXlke7Bgwdj+PDhiIuLg6enJ3R1dRW2V6lSpdCSIypq4qfK3h+ULVhtaRAREREpKLF+oKZTKDKazW+sdOw9NeZBlE3lortDhw4AgF69/lt0QJIkCCG4kBoRERERERHRW1QuuqOjo9WRBxEREREREVGxo3LR7ezsrI48iIiIiIiIiIodlYtuALh9+zZCQkIQFRUFSZLg4eGBIUOGoGzZsoWdHxEREZHa3X7goekUiKiQdPzRWvng5urLgyibyquX79mzBxUrVsSZM2dQpUoVVK5cGadPn0alSpWwb98+deRIRERERERE9ElSeaR7zJgxGDZsGGbMmJGjffTo0WjSpEmhJUdERERERET0KVN5pDsqKgoBAQE52nv16oXIyMhCSYqIiIiIqKjIzAQOHwbWr8/6kzfrISJVqFx029jYICIiIkd7REQESpYsWRg5EREREREVCVu2AC4uQIMGQJcuWX+6uGS1ExEpQ+Xp5X369MEPP/yAO3fuwNvbG5Ik4dixY5g5cyaGDx+ujhyJiIjoE7MwdL7SsYHB6stDWTsfLVI6tjvGqjETKkq2bAE6dgSEUGx/+DCrfdMmoH17zeRGeUv92krTKRApULnonjBhAkxNTTF37lyMHZv1j46DgwOCg4MRGBhY6AkSEREREX1smZnAkCE5C24gq02SgKFDgTZtAG3tj54eEX1CVC66JUnCsGHDMGzYMLx58wYAYGpqWuiJERERERFpSng48OBB3tuFAO7fz4rz9f1oaRHRJ6hA9+nOxmKbiIiIiIqj2NjCjSOiz5dSRXeNGjVw4MABWFpaonr16pAkKc/YCxcuFFpyVPx9atf8ERFR8dT92wRNp0BFjL194cYR0edLqaK7TZs20NfXl/+cX9FNRERERPSp8/EBHB2zFk3L7bpuScra7uPz8XMjok+LUkX3pEmT5D8HBwerKxciIiIioiJBWxtYsCBrlXJJUiy8s8efQkK4iBoRvZ/K9+kuU6YMnj17lqP95cuXKFOmTKEkRURERESkae3bZ90WrFQpxXZHR94ujIiUp/JCanfv3kVmZmaO9tTUVDzIb4lHIiIi+mw0sZmtQnQvteVB9KHat8+6LVh4eNaiafb2WVPKOcJNRMpSuujevn27/Oc9e/bA3Nxc/jwzMxMHDhyAq6tr4WZHRERERKRh2tq8LRgRFZzSRXfbtm0BZN2nu0ePHgrbdHV14eLigrlz5xZqckRERERERESfMqWLbplMBgBwdXXF2bNnYW1trbakiIiIiIiIiIoDla/pjo6OVkceRERERERERMWOykU3ACQmJuLIkSOIiYlBWlqawrbAwMBCSYw+D1xoh4hIsxaGzlc6NjBY+X6ruXRVOjZV+W6JPnmeJ6spHftvnQilY++OtFM+iebKhxLRh1O56L548SJatGiBpKQkJCYmokSJEnj69CmMjIxQsmRJFt1ERERERERE/6PyfbqHDRuGVq1a4fnz5zA0NMSpU6dw79491KxZE3PmzFFHjkRERERERESfJJWL7oiICAwfPhza2trQ1tZGamoqnJycMGvWLIwbN04dORIRERERERF9klSeXq6rqwtJkgAAtra2iImJgYeHB8zNzRETE1PoCVLxVrLUN5pOgYiI1CDoyA4Von9UWx5ERc0Ry7tq6Vf8NkzpWEktGRBRXlQuuqtXr45z586hfPnyaNCgASZOnIinT59izZo18PT0VEeORERERERERJ8klaeX//zzz7C3twcATJ06FVZWVujfvz/i4+OxdOnSQk+QiIiIiIiI6FOl8ki3l5eX/GcbGxvs2rWrUBMiIiIiIiIiKi5ULrqjo6ORkZGBcuXKKbTfvHkTurq6cHFxKazcqAh5fs1C6dgSFV4qHTtv1iKlY3/CFKVjiYhIOTVPzFQhupfSkW9GfK96MkSfgQrllL+iOl6NeRDRx6Py9HJ/f3+cOHEiR/vp06fh7+9fGDkRERERERERFQsqF90XL15E3bp1c7TXrl0bERERhZETERERERERUbGgctEtSRLevHmTo/3Vq1fIzMwslKSIiIiIiIiIigOVi24fHx9Mnz5docDOzMzE9OnT8dVXXxVqckRERERERESfMpUXUps1axbq1asHd3d3+Pj4AADCw8Px+vVrHDx4sNATpKLhQJV6Ssd+k6Z8v9UbbFI++LXyoQtD5ysdGxisfL9ERMXN+rWDlY6tO1WNiRDRB/mxYSOlY6cnn1RjJkT0LpVHuitWrIjLly+jU6dOiI+Px5s3b9C9e3dcu3YNlStXVkeORERERERERJ8klUe6AcDBwQE///xzYedCREREREREVKwoVXRfvnwZlStXhpaWFi5fvpxvbJUqVQolMSIiIiIiIqJPnVJFd7Vq1RAXF4eSJUuiWrVqkCQJQogccZIkqbSC+dGjRzF79mycP38esbGx2Lp1K9q2bSvfLoTA5MmTsWzZMrx48QJffvklfvnlF1SqVEkek5qaihEjRmD9+vVITk5Go0aNsHjxYjg6OiqdB73f0BpnlI79Ro15KOv7anc1nQIRERER0XvtDnJWOrbZvHtqzITURalruqOjo2FjYyP/+c6dO4iOjs7xuHPnjkoHT0xMRNWqVbFo0aJct8+aNQvz5s3DokWLcPbsWdjZ2aFJkyYKtywbOnQotm7dij///BPHjh1DQkICWrZsyduXERERERERkcYpNdLt7Oyc688fqnnz5mjevHmu24QQCAkJwY8//oj27dsDAFatWgVbW1v88ccf6Nu3L169eoXly5djzZo1aNy4MQBg7dq1cHJywv79++Hn51douRIRERERERGpSqmie/v27Up32Lp16wIn87bo6GjExcWhadOm8jZ9fX3Ur18fJ06cQN++fXH+/Hmkp6crxDg4OKBy5co4ceJEnkV3amoqUlNT5c/fHjknIiIiIiIiKixKFd1vX2edH1Wv6c5PXFwcAMDW1lah3dbWFvfu3ZPH6OnpwdLSMkdM9v65mT59OiZPnlwoeX4uvH4uvBkObzs8JFLp2I5oqHTs8P4tlI4NUzqSiIiIiKhwdQqqqHTsazXmQeqj1DXdMplMqYc6rqOWJEnhuRAiR9u73hczduxYvHr1Sv6IjFS+8CMiIiIiIiJSllJFtybY2dkBQI4R6/j4ePnot52dHdLS0vDixYs8Y3Kjr68PMzMz+cPU1LSQsyciIiIiImVlZgKHDwPr12f9yTWRqTgpUNF94MABtGzZEmXLloWbmxtatmyJ/fv3F2pirq6usLOzw759++RtaWlpOHLkCLy9vQEANWvWhK6urkJMbGwsrly5Io8hIiIiIqKia8sWwMUFaNAA6NIl608Xl6x2ouJA5aJ70aJFaNasGUxNTTFkyBAEBgbCzMwMLVq0yPPWX3lJSEhAREQEIiIiAGQtnhYREYGYmBhIkoShQ4fi559/xtatW3HlyhX4+/vDyMgIXbp0AQCYm5sjICAAw4cPx4EDB3Dx4kV07doVnp6e8tXMiYiIiIioaNqyBejYEXjwQLH94cOsdhbeVBwotZDa26ZPn4758+dj0KBB8rbAwEDUrVsXP/30k0L7+5w7dw4NGjSQPw8KCgIA9OjRA2FhYRg1ahSSk5MxYMAAvHjxAl9++SX27t2rMB18/vz50NHRQadOnZCcnIxGjRohLCwM2traqp4a5aPDgAfvD8p2TX15KOuQ8ToVomurLQ9ldV1aQ+nYtX0vqDETooJZGDpf6djAYPXlQURU1MVPlSkfHKy2NIqEzExgyBBAiJzbhAAkCRg6FGjTBijO/7V//fsJ5YOD1ZYGqZHKRffr16/RrFmzHO1NmzbF6NGjVerL19cXIrffsv+RJAnBwcEIDg7OM8bAwAChoaEIDQ1V6dhEREREH1tmJhAeDsTGAvb2gI9P8S4miPITHp5zhPttQgD372fF+fp+tLSICp3K08tbt26NrVu35mj/66+/0KpVq0JJioiIiKi44XWrRIpiYws3jqioUmqke+HChfKfPTw88NNPP+Hw4cOoU6cOAODUqVM4fvw4hg8frp4siYiIiD5h2detvjvBL/u61U2bgPbtNZMbkabY2xduHFFRpVTRPX++4rV6lpaWiIyMVLi/tYWFBVasWIHx48cXboZUJIy4mqR0bHc15qGsuZ3bKR/8Wn15KOtw1V80nQLRB/m+2l1Np0BUZPG6VaLc+fgAjo5ZXz7l9vshSVnbfXw+fm5EhUmpojs6OlrdeRAREREVS7xulSh32trAggVZsz0kSbHwlqSsP0NC+GUUffoKdJ9uIiIiIlIOr1slylv79lmXV5Qqpdju6MjLLqj4UHn18l69euW7fcWKFQVOhoiIiKi44XWrRPlr3z7r8gqu7E/FlcpF94sXLxSep6en48qVK3j58iUaNmxYaIkRfYjDQyLfH/Q/HaH5z+3vG79VPrj2PfUlQlRANUa8eH/Q//ATTJ8bXrdKH4PhuNqaTuGDaGvz8goqvlQuunO7XZhMJsOAAQNQpkyZQkmKiIiIqLjgdatERJ+3QrmmW0tLC8OGDcuxyjkRERER8bpVIqLPmcoj3Xm5ffs2MjIyCqs7IiIiomKF160SEX2eVC66g4KCFJ4LIRAbG4udO3eiR48ehZYY0eck4ESq0rEP1ZgHUUE1+m2a8sHN1ZcHUVHH61ZJXeLTymo6BSK1Wbx4MWbPno3Y2FhUqlQJISEh8MlnIYzU1FRMmTIFa9euRVxcHBwdHfHjjz++d1FwdVG56L548aLCcy0tLdjY2GDu3LkaOwkiIiIiIiIqfjZs2IChQ4di8eLFqFu3LpYuXYrmzZsjMjISpUuXznWfTp064fHjx1i+fDnc3NwQHx+v0VnZKhfdhw4dUkceRERERERERArmzZuHgIAA9O7dGwAQEhKCPXv24Ndff8X06dNzxO/evRtHjhzBnTt3UKJECQCAi4uL0sc7fLjwZyQpvZCaTCbD7NmzUbduXdSqVQvjxo1DSkpK4WZDRERERERExd6bN2/w+vVr+SM1NefllmlpaTh//jyaNm2q0N60aVOcOHEi1363b98OLy8vzJo1C6VKlUL58uUxYsQIJCcnK5VXs2ZA2bLAtGnA/fuqn1dulC66Z86ciTFjxsDY2Bj29vaYN28eAgMDCycLIiIiIiIi+mxUrFgR5ubm8kduo9ZPnz5FZmYmbG1tFdptbW0RFxeXa7937tzBsWPHcOXKFWzduhUhISHYtGkTBg4cqFRejx4BQ4YAW7YArq6Anx+wcSOQlqb6OWZTenp5WFgYQkNDMWDAAABZw/Zt27bF0qVLIWXfZJKKrfipMuWDg9WWhsZF/emgdKxHsPL97prGyzbo02b7TzMVok+qLQ8iIiL6NERGRqLUW/dR1NfXzzP23XpTCJFnDSqTySBJEtatWwdzc3MAWVPUO3bsiF9++QWGhob55lWiBBAYmPWIiABWrAAGDgT69we+/x4ICACqVlXyJP9H6ZHue/fuoWXLlvLnfn5+EELg0aNHqh2RiIiIiIiIPmumpqYwMzOTP3Iruq2traGtrZ1jVDs+Pj7H6Hc2e3t7lCpVSl5wA4CHhweEEHjw4IFKOVarBowZk1V0JyZmFeA1a2bd7vHqVeX7UbroTktLU/hWQJIk6Onp5Tr3noiIiIiIiOhD6OnpoWbNmti3b59C+759++Dt7Z3rPnXr1sWjR4+QkJAgb7tx4wa0tLTg6Oio1HHT04FNm4AWLQBnZ2DPHmDRIuDxYyA6GnByAr75RvnzUGn18gkTJsDIyEj+PC0tDT/99JPCtwjz5s1TpUsiIiIiIiKiXAUFBaFbt27w8vJCnTp1sGzZMsTExKBfv34AgLFjx+Lhw4dYvXo1AKBLly6YOnUqevbsicmTJ+Pp06cYOXIkevXq9d6p5QAweDCwfn3Wz127ArNmAZUr/7fd2BiYMQNQYUF05YvuevXq4fr16wpt3t7euHPnjvw5r+2m4s595Xm19Fv1WG3lgxu/UksORESfM3Wt2UFERB+mc+fOePbsGaZMmYLY2FhUrlwZu3btgrOzMwAgNjYWMTEx8ngTExPs27cPgwcPhpeXF6ysrNCpUydMmzZNqeNFRgKhoUCHDoCeXu4xDg6AKnfSVrroPnz4sPK9EhERERERERWCAQMGyBf0fldYWFiOtgoVKuSYkq6sSZMAb29A551KOSMDOHECqFcva1v9+sr3qfQ13URERERERETFWYMGwPPnOdtfvcraVhAsuomIiIiIiIgACAHkdtX0s2dZ13MXhEoLqRF97gJ3rVA6dlGdQWrMhIiICtPqexZKx05XXxpERKQh7dtn/SlJgL8/8PYdzDIzgcuXs6adFwSLbiIiIiIiIvqsZd+QSwjA1BR4e6FzPT2gdm2gT5+C9a1U0d2+fXuEhYXBzMwMq1evRufOnXO9eTkRERERERHRp2blyqw/XVyAESMKPpU8N0pd071jxw4kJiYCAHr27IlXr3jLIiIiIiIiIipeJk0q3IIbUHKku0KFChg7diwaNGgAIQQ2btwIMzOzXGO7d+9eqAkSERERERERqUuNGsCBA4ClJVC9eu4LqWW7cEH1/pUqupcsWYKgoCDs3LkTkiRh/PjxkHLJRJIkFt1ERYhVKxulY5/9/USNmVBx92bE95pOgYiI1ECvq6fSsWlr/1VjJkTq06bNfwuntW1b+P0rVXR7e3vj1KlTAAAtLS3cuHEDJUuWLPxsiIiIiIiIiD6iSZNy/7mwqHyf7ujoaNjYKD96RkRERERERPS5UvmWYc7Oznj58iWWL1+OqKgoSJIEDw8PBAQEwDx7nXUiIiIiIiKiT4ClZf7Xcb/t+XPV+1e56D537hz8/PxgaGiIWrVqQQiB+fPn4+eff8bevXtRo0YN1bOgQnPIs47SsQ3+PanGTKgoeFYzTdMpEBVJnierqRT/b50IteRBRFTUbb9WX9MpEKldSIh6+1e56B42bBhat26N3377DTo6WbtnZGSgd+/eGDp0KI4ePVroSRIRERHlJjMTCA8HYmMBe3vAxwfQ1tZ0VkRE9Cnp0UO9/RdopPvtghsAdHR0MGrUKHh5eRVqckRERER52bIFGDIEePDgvzZHR2DBAqB9e83lRaSKTJkWwmO8EfvGFvamj+FT+gS0tWSaTovos/L6NZB9R+zXr/OPzePO2flSueg2MzNDTEwMKlSooNB+//59mJqaqp4BERERkYq2bAE6dgSEUGx/+DCrfdMmFt5U9G2JaoUhu2fiwetS8jZHs4dY0Gw02nv8rcHMiD4vlpZZM6ZKlgQsLHK/vluIrPbMTNX7V7no7ty5MwICAjBnzhx4e3tDkiQcO3YMI0eOxHfffad6BlSoao2ooukUiIiKvHtO9ppOgT5AZmbWCPe7BTfw33+Khg7Nuu+qslPNea95+ti2RLVCx42r8e7H+OFre3TcuBqbOnVHUfje6GWbm5pOgUjtDh4ESpTI+vnQocLvX+Wie86cOZAkCd27d0dGRgYAQFdXF/3798eMGTMKPUEiIiKit4WHK04pf5cQwP37WXG+vh8tLSKlZcq0MGT3zP8V3Ip38BXQggQZhu6egTaZXKOA6GOoXz/3nwuLykW3np4eFixYgOnTp+P27dsQQsDNzQ1GRkaFnx0RERHRO2JjCzeO6GMLj/FWmFL+LgEt3H/tyC+OiDTkxQtg+XIgKipr9pSHB9Cz53+j4arSen9I7oyMjODp6YkqVaqw4CYiIqKPxl7JqwOUjSP62GLf2CoXxy+OiD66I0cAFxdg4cKs4vv586yfXV2zthWEyiPdRERERJrk45O1SvnDh7lf1y1JWdt9fD5+bkTKsDd9rFwcvzgi+ugGDgQ6dwZ+/fW/yzsyM4EBA7K2Xbmiep8suosZ164blY6Nx1I1ZkJEVHRV97FWbYdo9eRBBaOtnXVbsI4dswrstwvv7BVnQ0J4LSwVXT6lT8DR7CEevraHyGXiqQQZHM0ewcfHUQPZKfraqZemUyD6qG7fBjZvVvw3RFsbCAoCVq8uWJ8Fnl5OREREpCnt22fdFqzUO5fFOjrydmFU9GlrybCg2WgAWQX227KfhzQbwy+OiDSgRo2sa7nfFRUFVKtWsD450k1ERESfpPbts24LFh6ede2rvX3WlHIWKvQpaO/xNzZ16p7LfbofIaTZGN6nm+gjunz5v58DA7NuS3nrFlC7dlbbqVPAL78ABb1ZV4GK7jVr1mDJkiWIjo7GyZMn4ezsjJCQELi6uqJNmzYFy4SIiIhIRdraXN2ZPl3tPf5GG/edCI/xRuwbW9ibPoZP6RPQ1pK9f2ciKjTVquW8XGnUqJxxXbpkXe+tKpWL7l9//RUTJ07E0KFD8dNPPyEzMxMAYGFhgZCQEBbdRERU5EXd2qHpFFT2/JqF0rElKrxUWx5EVLi0tWTwdTmm6TTy9PqnDKVjTf3VlweROkWree0WlYvu0NBQ/Pbbb2jbti1mvDW+7uXlhREjRhRqckRERERERETq5Oys3v5VLrqjo6NRvXr1HO36+vpITEwslKSIiIiIiIiINCUyEoiJAdLSFNtbt1a9L5WLbldXV0RERMD5na8D/vnnH1SsWFH1DIiIiIiIiIiKgDt3gHbtgH//VbzOO/uWlP+7ulolKhfdI0eOxMCBA5GSkgIhBM6cOYP169dj+vTp+P3331XPgIiI6COLn6riIkXBaklDJZXndFc69hH/OSaiQlLq+wEqRH+vtjyIPpYhQwBXV2D/fqBMGeDMGeDZM2D4cGDOnIL1qXLR3bNnT2RkZGDUqFFISkpCly5dUKpUKSxYsADffvttwbIgIiIiIiIi0rCTJ4GDBwEbG0BLK+vx1VfA9OlZtxO7eFH1Pgt0y7A+ffqgT58+ePr0KWQyGUqWLFmQboiIiIiIiIiKjMxMwMQk62dra+DRI8DdPWuxtevXC9ZngRZSy8jIQLly5WBtbS1vv3nzJnR1deHi4lKwTIiIiIiIiIg0qHJl4PLlrKnlX34JzJoF6OkBy5ZltRWElqo7+Pv748SJEznaT58+DX9//4JlQURERERERKRh48cDsv8t/TJtGnDvHuDjA+zaBSxYULA+VR7pvnjxIurWrZujvXbt2hg0aFDBsqBCo9LiQMFqS4OIiArZ9a9y/ttLREREhcvP77+fy5TJunXY8+eApeV/K5irSuWRbkmS8ObNmxztr169QmZB1k8nIiIiIiIiKgJ69QLeLXdLlACSkrK2FYTKRbePjw+mT5+uUGBnZmZi+vTp+OqrrwqWBREREREREZGGrVoFJCfnbE9OBlavLlifKk8vnzVrFurVqwd3d3f4+PgAAMLDw/H69WscPHiwYFkQERERERERacjr14AQWY83bwADg/+2ZWZmXdNd0Jt2qVx0V6xYEZcvX8aiRYtw6dIlGBoaonv37hg0aBBKlChRsCyIiIgoX2W79VM6Nh6d1ZgJERF9CnYHOSsd22zePTVm8mmwsMi6ZluSgPLlc26XJGDy5IL1XaD7dDs4OODnn38u2BGJiIiIiIiIipBDh7JGuRs2BDZvzrqOO5ueXtZ9uh0cCtZ3gYpuAEhKSkJMTAzS0tIU2qtUqVLQLomIiIiIiIg+uvr1s/6MjgZKly74SuW5UbnofvLkCXr27Il//vkn1+1cwZyIiIiIiIg+FZcvA5UrA1pawKtXwL//5h1bkDFmlYvuoUOH4sWLFzh16hQaNGiArVu34vHjx5g2bRrmzp2regZERERERERUqJqZvdR0Cp+MatWAuLishdKqVcsa5RYiZ5wkZS2qpiqVi+6DBw/ir7/+whdffAEtLS04OzujSZMmMDMzw/Tp0/H111+rngURERERERGRBkRHAzY2//1c2FQuuhMTE1Hyf2ullyhRAk+ePEH58uXh6emJCxcuFHqCREREREREROri7Jz7z4VF5aLb3d0d169fh4uLC6pVq4alS5fCxcUFS5Ysgb29feFnSERERERERPQRrF6d//bu3VXvs0DXdMfGxgIAJk2aBD8/P6xbtw56enoICwtTPQMiIiIiIiKiImDIEMXn6elAUlLWbcOMjD5S0f3999/Lf65evTru3r2La9euoXTp0rC2tlY9AyIiIiIiIqIi4MWLnG03bwL9+wMjRxaszwLfpzubkZERatSo8aHdEBERERFRIZHJJMTGOCAxwQjGJkmwL/0IWlq5LMdMRO9VrhwwYwbQtStw7Zrq+ytVdAcFBSnd4bx581TPgoiIiIiICsXta2UQvscHiW9M5W3Gpm/g4xeOshXuaDAzok+Xtjbw6FHB9lWq6L548aJSnUmSVLAsiIiIiIjog92+Vga7NzXP0Z74xgS7NzVHs47/aCArok/H9u2Kz4UAYmOBRYuAunUL1qdSRfehQ4cK1jsRUXEXbK5C7Cv15UHFXvxUmfLBwWpLQ+NuhbdQOtbNZ5caM6GiokUbY6Vjd/2VqMZMNE8mkxC+x+d/z94dDJMACBzb64PMzKxRO1KNn0+s0rF7wnlXp09V27aKzyUp6x7eDRsCc+cWrE+Vrum+d+8e9u7di4yMDNSvXx8VK1Ys2FGJiIiIiKhQxcY4KEwpz0lCwmtThIcDvr4fKyuiT4tMhe+4laV00X306FG0aNECSUlJWTvq6GDVqlX47rvvCj8rIiIiIiJSSWKCkVJxscoP2BJRIVC66J4wYQIaNGiApUuXwtDQEGPHjsWoUaNYdBMRERERFQHGJklKxdlz5jNRnlRYQxzKriGudNH977//4ujRo3BwcAAAzJ07F7/99htevHgBS0tL5TNTQUZGBoKDg7Fu3TrExcXB3t4e/v7+GD9+PLS0tAAAQghMnjwZy5Ytw4sXL/Dll1/il19+QaVKldSS0+cq6k8HpWM9gtWXBxEREdHbAstaazqFIsO+9CMYm75B4hsT5LymGwAETMwS4OOT3xR0ysvwG7+oED1NbXmQel28CFy4AGRkAO7uWW03bmStg/D2nbJVWUNcS9nAly9fomTJkvLnxsbGMDIywsuXL5U/mopmzpyJJUuWYNGiRYiKisKsWbMwe/ZshIaGymNmzZqFefPmYdGiRTh79izs7OzQpEkTvHnzRm15EREREREVNVpaAj5+4f979u49ubOef9U0nIuoEeWjVSugfn3gwYOs4vvCBeD+faBBA6BlS+DQoazHwYPK96nSQmqRkZGIi4uTPxdCICoqSqHArVKliipd5uvkyZNo06YNvv76awCAi4sL1q9fj3PnzsmPHxISgh9//BHt27cHAKxatQq2trb4448/0Ldv30LLhYiIiIioqCtb4Q6adfwnx326TcwS8FVT3qeb6H3mzgX27gXensxtaQlMmwY0bQoMH656nyoV3Y0aNYIQit+atWzZEpIkQQgBSZKQmZmpehZ5+Oqrr7BkyRLcuHED5cuXx6VLl3Ds2DGEhIQAAKKjoxEXF4emTZvK99HX10f9+vVx4sQJFt1ERERE9NkpW+EOXMtHZ61mnmAEY5Mk2Jd+BC2td0e/iehdr18Djx8D716tHB8PFHQytdJFd3R0dMGO8AFGjx6NV69eoUKFCtDW1kZmZiZ++ukn+eJt2aPutra2CvvZ2tri3r17efabmpqK1NRU+XNORSciInXIzATCw7NWCra3B3x8eG9cIvo4tLQESrk81HQaRJ+cdu2Anj2zRrxr185qO3UKGDkS+N/kapUpXXQ7OzsX7AgfYMOGDVi7di3++OMPVKpUCRERERg6dCgcHBzQo0cPeZz0zlXs2aPueZk+fTomT56stryLo2bzGysdm/fXHUSkaYmrlJ8BZNxjqRozKb52ew0CABx/URVL7nfE0/T/5qdZ675AP6dNqGt5CQDQ7NwijeT4KbO/46R8sI/68qCio07zsZpOgT4TPV1/Vzr2oQoLqS0Mna90bGCw0qFUQEuWACNGAF27AunpWW06OkBAADB7dsH6VHohNU0YOXIkxowZg2+//Raenp7o1q0bhg0bhunTpwMA7OzsAEDhOnMAiI+PzzH6/baxY8fi1atX8kdkZKT6ToKIiD47x19UxbQ7vfE03UKh/Wm6Babd6Y3jL6pqJjEiIiLKl5ERsHgx8OzZfyuZP3+e1WZsXLA+i3TRnZSUJL81WDZtbW3IZDIAgKurK+zs7LBv3z759rS0NBw5cgTe3t559quvrw8zMzP5w9SUt00gIqLCkSkkLLnf8X/P3p11lfV86f0OyBQq3Gvk3WPItHD47ldY/28HHL77FTJlRfqfcyIiok+OsTFQpQpQtep/xXZ8fMH6UmkhtY+tVatW+Omnn1C6dGlUqlQJFy9exLx589CrVy8AWdPKhw4dip9//hnlypVDuXLl8PPPP8PIyAhdunTRcPZERPQ5uprgpjClPCcJT9JL4GqCG74uQP9bolphyO6ZePC6lLzN0ewhFjQbjfYefxegRyIiIjIyAu7dA2xssp43awasXJm1JguQtbiag0PWei2qUqnoFkIgJiYGJUuWhKGhoepHU1FoaCgmTJiAAQMGID4+Hg4ODujbty8mTpwojxk1ahSSk5MxYMAAvHjxAl9++SX27t3L0etCNrdzO+WDX6svD1JN1J8OSsd6BKsvDyo6jgUsVDrWr8f7Yygn3W9TgClKxqloS1QrdNy4Osfddx++tkfHjauxqVP3Yl94f1PtptKxu9SYBxUd5sdHKx/cpJ/6EqFib/pL9ayE+WNmb6VjA9FLLTkQkJICvH2jruPHgeRkxRhRwBsAqFx0lytXDlevXkW5cuUKdkQVmJqaIiQkRH6LsNxIkoTg4GAEBwerPR/KkimAa5kWeCH0YSmlooL2S2gXfJYkEVGxUtLqeaHGZcuUaWHI7pn/K7gVp5MLaEGCDEN3z0Ab953gAulERESFL5+1uvOlUtGtpaWFcuXK4dmzZx+l6Kai53S6DVamlsMzYSBvs5JS0FP/Jr7UfaLBzIiIigavKldhZ/MEj59YQeSydIoEGexKPoVXlasq9Rse460wpfxdAlq4/9oR4THe8FU1aSIiIlIblVdemTVrFkaOHIkrV66oIx8qwk6n22BOSmU8E/oK7c+EPuakVMbpdBsNZUZEVHRoa8swPnApAAkSZArbsp5L+HHwMmhry3LdPy+xb/K+K0dB4oiIiOg/kqQ4kv3u8w+h8kJqXbt2RVJSEqpWrQo9Pb0c13Y/f67adDn6NGQKYGVq9uyG3FbjFViZWg5eOhztLkrcV57XdApFUmYmEB4OxMZmLY7h4wNofybzcbul2ikdG48Xasyk+Oqg3xBoCpTW3Y5H8xoiPf6/NUZ0bBPgMOwQljUwwDI0xCUV+rU3fVyocZ+qc5UvajoFIvpMdf82QS39trAJVEu/pBrx/+3dd3xUVfrH8e9MegVpofduUIpIM4AoXVARdEXFXlFBrKyFyArIuiJgYYVVkJ9iA3TFVYoiGEAsFEWq0g2JKDUkmJDM+f1xySSTAjMhN8kkn/frNa/k3nvyzJMhd5jnnnvOMVLz5jmF9okTUrt2UvZiWkUdzy0Voeg+0/jq8q4if1DfllXZ45by/Bw6ZEK1LatySaUEFMnChdKoUdJvv+Xsq1tXmjZNGjKk9PJC+VPp0l8U3f1XpW6sq1N/RiioWqoi2v4mR0DR/teOq79GdaMTlXi8VqG3rdeNPqC4+mvONXUAACqc2bPti+1z0X3zzRVzOtuK/kH9SJ5bys+1HVAaFi6Uhg7Nf6UyMdHaP39+xTifUXIcAUaRHfYXS6wAp0vT+j2uoR/MlUMuj8I7+zb2qf2eUIDTt9vWAQCAZGeZ6/OYbknauXOnnnrqKV1//fU6eHqF8MWLF2vzZt8mhfEX2R/UcxfcUs4H9YULSyevknSeI71Y2wElLSvLunBW0K1B2ftGjy7a2otASRnSapHmXztCdaKTPPbXjT5QIZYLAwDAH/lcdK9cuVJt2rTRt99+q4ULF+rECWtsw08//aRx48YVe4KljQ/qlpYBR1XV8ZeUb3XYbEZVHX+pZcDREswK8F5CQv4LZ7kZI+3fb7UDyrIhrRZpz6hYfXXzQM0bcpu+unmgdo9qQ8ENoERkuZxasecSvbvpGq3Yc4myXEXqwwMqFJ9vL3/iiSf03HPPacyYMYqKypkc5tJLL9W0adOKNbmywJcP6j17llhaJS5h9BZ13PaXFs/vL6vwzj2ZmlWId7xmuRJa7tJ16lUaKaIA9/93lddtX+vSx8ZMSl9S0tnb+NLOXx38hw+3Hsfblka59twPx7xvfHHRniPA6VLPht6f3wBQHBZuHaRRiyd7LF9YNzpR0/o9zoW/c5D43zalnQJs5vOlqU2bNunqq6/Ot7969eo6dOhQsSRVlvjzB/XivhLZpOUu9Rv6uSKiPGdujIw+oX5DP1eTlrvOKT5gp1q1ircdAAAVycKtgzT0g7n67bjnf5SJx2tp6AdztXDroFLKDCj7fO7prly5spKSktSoUSOP/Rs2bFCdOnUK+Sn/5a8f1O26Etmk5S41ar5bSftqK/VEuCIi01Sr/gE5necwhz5QAuLirMkPExMLHi7icFjH4+JKPjcAAMqyLJdToxZPPn1vo2cnjpFTDrk0evHzujKr4qzsA/jC56J7+PDhevzxx/Xhhx/K4XDI5XJp9erVeuSRRzRixAg7cixV/vhBPftKZN50s69EnutkO06nUZ2GieeWJFDCAgKs1QaGDrXO29znc/Z6jFOn8mEBAIC8EvZ19ejIycvIqf3H65b74ZaoGLKypDlzpC+/lA4elFx5RuYtX+57TJ+L7gkTJuiWW25RnTp1ZIxR69atlZWVpeHDh+upp57yPYMyriQ+qBfn+t9eX4ls8T9RW+BctPmmrddtN3XZaFsevhgyxFoWrKDl/6ZOLVvLhR1b9m+v21bqfY+NmcBXgw5O96H1P2zLo7xiXgKgZE1/+SWtS2/sVduyONzSH/RZ8LP3jVt733Tre7W9btsq3vu45d2oUVbRPXCgFBubU/OdC5+L7qCgIL3zzjsaP368NmzYIJfLpXbt2qlZs2bnnk0ZZecH9eJe/9vrK5H7uqqn7+EBvzdkiHTllcV3oQvlQ5bLqYR9XZWUEqNaUb8rrv4a1rsGgNOinWletStrwy2BonjvPemDD6QBA4ovps9Fd7YmTZqocWPrqpejOMr/Ms6OD+rZ63/nvW09e/3v+fN9L7yTUmKKtR1QHgUEcPsbcjAbLwCcWZPA31XZcUJHTYQ8V7DJZlTZkaq4uMiSTg0odsHBUtOmxRuzSNNZv/HGG4qNjVVoaKhCQ0MVGxur//znP8WbWRmU/UH9+uutr+d6S7kd63/Xivq9WNsBQHnGbLwAcHZOh9GQiG9Pb+X98GptD4n4lrvGUC48/LB113FBdVpR+dzT/fTTT+ull17SAw88oC5dukiSvvnmGz300EPas2ePnnvuueLLrhyza/3vuPprVDc6UYnHa8kUcE3FIZfqRh9QXP01vicNv+MM2uFDa9/W6X701j+8b7zNp9C2SH3rbq/bRtz8uo2ZeKdZr7Fetz0oxnQXBXNgACXr1wTv79VsGveZjZnAF72rv2B9lXRhSm9NPDhWv2fmXKisGZissTUmqXfUMklbSydJP3cwo4ktcV+79HzvGhqjl43JGbycnCz9/ruUlpbzSE3N+XrrrVLk6bsaPvhA+vxz617sYcNs+T1K2qpV0ldfWb/W+edLQUGexxcu9D2mz0X3jBkzNGvWLF1//fXufYMHD9YFF1ygBx54gKLbS3at/x3gdGlav8c19IO5csjlUXg7ZI1PnNrvCcYqlnGMLwXsxxwYAOCb3lHL1CvyS6072UF/ZFZX9cA/1CFsnQIcfEaxjTEKzMpU0KlTCj6V4Xls40Zp7978hfHpR0CWS1kBVi3Q68cdOn9vsoIzMxV8Ksv6mpmloMwsBWdmSc8dkqpVs+KOHy/NmFF4TldckVN0r1tnzTpWvXq5KborV5auvrp4Y/pcdGdlZemiiy7Kt79Dhw7KzMwslqQqAjvX/x7SapHmXzuigDGKBzS13xOMUSzjGF8KlAzmwAAA3wU4XLo4/PvSTqNsMEbKknRKUoaRThnr+6w89yX/73/Szp0FF8cZGVLTbu6m137+gVr/ulnBmVaRHXwqQ87c9zn/6/Gcrtd//lN6991C0wu+dZBOBgRLkqofO6HmB85wl2Jqak7RXbWqVLOmFB5uPSIipLAw62t4uDXoOVv//lKVKlLnzmd/vfzE7NnFH9PnovvGG2/UjBkzNGXKFI/9M2fO1A033FBsiZV3dq//PaTVIl3Z4n/0lvoZr9ZYL5XMgPKHOTAAoPwLP5mqkIx0BZ/KcPcWh5zKUNCpDJm8k0G/9pr066/5eoyVliaFhkpdcrX9v1RpVyGTLwVJyj3d1WuvSZ8VPmTC8fcuMk6rRzriZKqqHTtcYLtMZ4AC09KkSpWsHS1aSJ065RTD2QXy6e9dh7a7f3Zti4b6pXZ1ZQQGKCMo0PoamP01QBPq1s15on/8w3p4o2fPcjs77R9/SNu3W3VZ8+ZWZ35RFWn28jfeeENLly5V59NXNNauXav9+/drxIgRGjNmjLtd3sIcOUpi/e8Ap0s9G646pzxRcrweX5pVNpa3emSzd8uHSNIIG/PwVqMbP/C67UF5P6Z7+ssved32wXivm7IWcQlgDgygZD34r5Vet/2siJ0O8BMul9XDGxqas2/jRun48fzFblqaFB0t3XZbTtuHH5Z27y54zHGdOtJlw91NR/3fdNU5eKDANI5GVZI0M2fHO+9Iawp5z4+KkrrkKtLzTuIeIKvYDnJYj9xjpLt3t34+T1Gc3XvsOJDp7nD5rHt/Le90qU4FBSsjMEingoKVHhSsjKBguQIC9Ep2wS1J48ZZj0J0jl5e6LF85paBD5dlRGqq9MAD0ty51p+qZH32HjFCevll65/OVz4X3T///LPat28vSdq5c6ckqXr16qpevbp+/jlnYfeKsIzYubJz/W/4H6/Hl/o4uR6AgjEHBgAUICtLOnnSXcwGnjqlzFwzSUWlpMjpcslhXHK6jPur07ikKVOkXB1wGjjQmpQrd89xaqr011/SxRdL336b03bwYGsW4YK0auVZdC9ZIm3eXHBbl+d7dkZQsE4FBCrjdOGaERSsU0FBSg8K1omIKFXO3Xj4cOs20wJ6jRUZKX37t5y2V4VZX4McVrHtzFP75K6FHn+84FyzU376Fff3v1ereca2sN+YMdLKldKiRVK303f+r1olPfigdb3nTMPdC+Nz0f3VV1/5/iwolB3rf8M/eT2+1MfJ9QAUjjkwAPiVzMycgjjPLdC1f0/UgRjrfczpylLP71Yo+NQpBZ9KP/01w/1Q0GHpmWesmMZIDRtKJ05Ysf76y+Mpa4VHaH+9ejnbyUkKcBVyMXL+fM+i+8cfrbGUBUnLc8dckyYFF7vh4VKu55ckjR0rpaR4tsn+mago6Z0v3E2n3PKQjKPwVZJfyb0xcmSh7SRJua4RKLJIKy/DDyxYYP0p5+7kGjDAujHh2mtLqOhG8cte/xsVm9fjS4swuR6AwjEHBoBilZl5xhmltS9Tqn/6I3i6kb78y5p8K3sSLo+vT0oTJlhtjx/PGctbgH6t2unNa6zeYONwaMgXHxee49pc43cdDunQISvPvMLC8t1GnRYeLofLyOV0yDic1lenUy6HU9Vy90ZL0n/+YxX12QVx9mRc2du5+dKxd9Z5pHKK7jMV3EBB0tKkmAL6wmrUyH+tyFsU3UAZ4fX40ri6Bfw0gHPBHBhAOZd1upBNTbUKPsnqKf3uu4LHEKelSV27Sr17W20TE6V7781/m3T293ffLU2ebLVNSpKaNi08l/ZBOUW3kfT9qcLb7tqV833uItXh8OzlDQvTsaicgtw4nPrmws7KcgYoI8hzXHBGULCGj85THK9ebc2InbsgDg2VnE7tb9nKo+lvdQr/HFLtjjs8d/TrV/jv5udY3rX86tLFGio/d27OtAMnT0rPPmsdKwqK7nLGrkmdYD+vx5cGvF1aKXrwt4m+/C1fAChv1lS6Jv9OYxTkcik8K0vhmZkKy8xUeGamdPCg1a0kWUXs558XPKN0WpqUcUpqdnrM8e9Z0sKT+XuNs/8LCH8x57bqPXukyy8vPOFHH80puk+dsgZ4Fub48Zzvs8f/FnartCvX5FZBkuKCrXHBwcqZhCt7Qq6xT+e0DQyU/vwzpyDOM3/Sgqc9bpTWO4MK7w0env17ZbvwwkLb9nvpDK9RHnu9bunf/GV513WnnvS67VB9Y2Mm/mXaNOt6Ud261qnhcFjz/IWGWtMJFAVFN1CGML4UAFCmZGZaPcJ5C97s7XbtpPr1rbbbtknz5uUvik+37etwaMnpZYm6JyXp7ZUrFZ6ZqcCC1k597z1r1iLJWsLp9tsLz7FXiNTs9PdG0sEzXGTNfW9opUpSbKxnQZz7kXvd4erVpVmz8hfQ2UV11ao5batWtV6zwsTnukU8wCH1Ci28bevWntu5nwelwqvlXfm85tdiY6VffpHeftt6WzNG+tvfrFENYWFFi0nRDZQxjC8FAJxVdqGa3dt5+LDVc1tIwavBg6VGjay2CQlWAVnYbdWvvGLNGiRJH35ozehcmLfestbRkazi+Axr+9a7+GL391lOp6JPed5WnelwKDUwUH8FBCgm12zZiomxZsEuYKklhYdLO8bntK3ilG4Ktz7hBjs8l3AKljR+Uk7b+vWlTZsK/91yi4iQ8t46jQrH6+VdW/xPzIns38LCpDvvLL54XhXdn3zyidcBBw8eXORkAFgYXwoAfszlsmaAzi56a9aUQkKsY7/8Iv38c8ETbKWlWb27DRtabefPtxaFLey26i++kHr1stq+/750332F59SoUU7RvXev9H//V3jbw4dzvs8eRxwUVPDt0pUr57Rt0sSa/Tl3QZyr/Zfvvutuur5qVbW78kqdDAzUycBApQUE6FSupVuO3ntvTtzmzaVPPy083/iJOd8HO6TGZ/h4y5K2OAdeL++6r6t6llxasMGOHdKKFdZIl7yT9WePUPGFV0X3VVdd5bHtcDhkct0KlHtN7qysLN+zQLHpXf0FH1rfdvYmAErFvcOmeN3Wl5UrmPcBFZ7LZc2Ik6eQbbL3V+2t00CZgVYPa+P9u9TgwB73UktBpzIUcvqrNi+Xpk+3BvxJ0tSp0ksv5RTSJ096Pue6dVL79tb38+dLf/974flddVVO0f3779LXXxfeNvet0lWqSHXqFHzrc3i451S8HTpIL7yQv112sZx7ErCBA6WMDKvoPptWraxe8kJMeW6qJO9W6igqJreCnbxe3tXLdnZLeeRss7yjILNmWfMmVqtmXTPNfa3O4bCx6HblKu+/+OILPf7445o4caK6dOkih8OhNWvW6KmnntLEiRPPEAUAAMALR49KR44UfJv0jxlSbJA1FlaSNp+S9mVZE2Zlnp40K+P010/aS8uW5YyDHTXKKpYL8JCkcSPH6dB51SRJsb/8rD5rlhWc388/WFPbZhfdqanSvn0Ftw0NldLTc7YbNZK6dctfHGcXvLVr57Tt21f64APPgjh3IX3eeTltr7vOenijVSvr4Y1A/xmJ6C+TW8F/eb28q5ftUDY995y1Ut/jjxdfTJ/fSUePHq1///vfuuSSS9z7+vbtq/DwcN11113aunVr8WUHAADKBIfLJZ04Yc3KnG3bNik5Of+t0qmp6puwQkvicpYL6rX2SzXZt9Pdc5z7odfGSQcO5NyCPXKkNSFXYZoHSdmT2ezOlNYVsuTS/g3WhFbZRXdwsOfxsDB3MZucli5Hrrv49tesq+/Pv0ingoKUHhTsXnLpVFCwhlzTx7M4vvlmqU+f/Ldeh4VJAXlGdv7tb9bDG02bnnnpKbgxuRVKgtfLu9ZfUwrZobgcOSING1a8MX0uunfu3KlKlSrl21+pUiXt2bOnOHICAAC+yMqzPNL27VKLFjnHly2z1hkuYH3ha777WQv65CzldO3nH6jxb7s8iuKgU6cUlJUpvfSE5y3Njzwi/e9/BaY0SNKybr3lclpFZ/0D+3ThjjNMWnXyZE7RHRlZ6LhgHVhzehKx0z3dTQOlsDzLLGUvvXTLAmvW6WxPPik99lhOQezM+dD8XJ7llja0bq8NrdsXmOqQe+7x3FG3bk6vN0ock1uhpHi9vCtDGvzasGHS0qVS3rf6c+Fz0d2xY0eNHj1ab7/9tmrVqiVJSk5O1sMPP6yLc81KidLx8lXTvG77mo15ADg3Ae+dPHujbIVPFpxPxzZeztSL4pO3ID5lcr7/+mupe/ectrNmeRbHuQvkfSelq3OtVTI3VTqQ5bkGcbb5l1o9x9nGj5dWFTw5Y9egYI+iu+rRP1X398SCf5eTJ60x0dnFasOG1m3KBaxFvGrLbjldLnfRvbZtF+1o2FwZQcHKyNVznBEUrCceudWzB/3f/5Zef73gHOLzXPhvGWQ9CtK/v+d27km/UOIuvWaLLXGZ3AolieVdy7+mTaWnn5bWrpXatMk/pUX2aoa+8LnofvPNN3X11VerQYMGqn96XcZ9+/apefPm+vjjj33PAACAsuCw6/RY4AKK5I8/tia4yvb449aUpgX0HOtUmnRdeE7bl09IxwpYh1iSvrtbyj0sa/p0a2brgkTlmXX5lKT0PG0csnp7IyI893fuLEVFeY4dPl0kf77mJ4+mn3UfoK8uvtQqjoOtojgj0CqSJz/3oOeMMmeYNOu9PD3H2xq3LLStWuY5xgzT8IG/TW4F/8fyruXbzJnWdeCVK61Hbg5HCRXdTZs21U8//aRly5Zp27ZtMsaodevWuvzyyz1mMQcAoMiMsSafSkuz/ofLnjDK5ZK+/LLgnuC0NGvJoptvzokzeLA1pjfvkktHjkv1AqQbcxWnM0/kL2KzHZzsWXTPmyf99lvBbavnGecX5JBkcgri3LdBN2vm2XboUOmSS/L1GisiQlrygGfbq8Os1yko163VAbJer/hfPNu+UPjKFl/kKY731mlYaFuFhRV+DCglTG6F0sDyruXX7t3FH7NIU1I6HA716dNH3bt3V0hICMU2AFQkxuSsQXz6UTdpv06Ghrlnfg7MPKWLf/pOwZmeyy0Fn8qQ9n4ndepkTZYlWbcsX3RR/jWIs1fOGDpU+vBD63uHw5qwqjB9+3oW3V99ZU3+VZCMPNuRTinI5CqMcxXHuSYPlSQ9+qj1GhS0NNMHV3q2vSPC+t/Wqfw9uPGfeG6PG1f477Z/jOd2lfyT+AAVEZNbASjrfC66XS6XJkyYoH//+9/6/ffftWPHDjVu3FhPP/20GjZsqNtvv92OPAEA3srKkg4fzl/EpqWpyp+/6q/QSkqLtCaXCjx1UrV+2yBn1ik5XZkKOP3VmXVK6r7O6t0dc7rYO3DA6pk9efL0RFY5npC0um0XvXvFcCtuZqaGf/ZewfmtX22t+5tddAcHS1vOMNYz93JLDod08cXWmOK8xW54uBQb6/mzM2dabfP2HP+nmxSS53nuj1Sh4vP0FJ/p3rKEPFM1hXBh2h+5XA4l7aut1BPhiohMU636B+R0FjJMAKWKya0AFKesLGnOHOvGuoMHc/oAsi1f7ntMn4vu5557Tm+99Zb++c9/6s4773Tvb9OmjV566SWK7lLW6zUf/kmfty8P+Gb6yy953fbBePvygO/u/9TLpX8k613b5cpZ9/bECWvJpQJulb702+XaVbex+1bfqkf+1MCV/3P3FgedynAvvaS3/ik98IDV+ypZY4TbtCkwhRaSvuh8mT6O6yxJOu/oYf1jTSETViXs9VzLNzTUc+ZqySqYw8N1JEtKDwl1784ICtamZrHuibJOBQUrPdgaG3zFoEul1q1zYgQEWD3SYWEFL7mUdwaTb78906vs6frrC95PLzHOYOe2xkpYEqfUlCj3voioFMX1TVCTlrtKMTMUhsmtABSXUaOsonvgQOtafnHc1O1z0T137lzNnDlTl112me7JNY/6BRdcoG3btp17RgBQGlxGypQ1kdbu3da6vtHR1rGkJGn16vw9x6mpcuw8JtNriHRhV6vt1vVyTHtM+itN+uuklH76619pUvpJaepU691ckn78Mf9ty6ddI2lRj4HuojskI10X//xDwbkfkvTnnznb2ZNohYZ69u6Gh2vnn8d0JLqyu+nJsDCt6NjDPaN0RlCQMoJCdCowSDeNuNJzjeDKlaVdu3KK4fBw9wWEp/OMC3YFBOj16+4uMN0rHrk//86ePQv+3YAStnNbYy2e3z/f/tSUSC2e31/9hn5eClnBG0xuBaA4vPee9MEH0oABxRfT56I7MTFRTXN/CDvN5XLp1KlTxZIUAHjIzCzwVmmlplqXILPX4d261VozuKBJtjakSV2CpQan3/Z2nJL+99fp2aolZeV6vhcbW5c4s8cGr19vLdpYAIckU7dJTtH9V5ocP31T+O+Su6c4OlqqVy9fYayICH3/y29Krl7T3fRYVGUtvPxqZZxeZumUu0AO1ugHb5Lq5Foup2FD6zULyL8i7Ut5iuO/QsI0v+/QAlO9aWie/U6n1KhR4b8b4OdcLocSlsSd3srbtWFNiLdqaZyysgo8vVAGMLkVgHMVHOzZ51AcfC66zz//fCUkJKhBgwYe+z/88EO1a9eu2BID4GfSjZRqPNcgzv46Z461Xm7M6eVavv7amhgr74zS2Y8ZM6QuXay2M2dKdxfcYyrJWsrpytMTV23YkHOLdUFaBUrZb11G0vFCxmeGh1sDerLVrJl/RunT35vDQVLLXO99jVrKNXGeFBomhYRLoeFSWLgUEi7nJVWkSrnWGG7TRtq3r8AU3spTHKeGR2h5514F53vRRZ7bDgcVAVAESftqe9xSnp9DJ45HKSGBmzMAoLx6+GFp2jRrVczimi/c56J73Lhxuummm5SYmCiXy6WFCxdq+/btmjt3rj799NPiyaoCqJF1ntdtDwYc8brtilFnmIwoj6Eq5AM8StyLb2/wuq3XSwMao4CsTAWfytBfwaEyTmsMa9Ujf6rq0UMKPj022BoXnC7983TBe999Uo0aVoz335fefjv/skxpadLh49LNEVKt08Xd9xnSl4WstzT/VmvMbnbR/fPPZ1zf1+NW6dxLFDkc+WeLDsk1G1bTptJNN+VvExYmfTlWqpurEG0QKN0ZIQXLmp06UFLw6a/PHvfMp0MHKSGhwFRHPrFU2pUp7crds1JVOpb9fdrph/TakDOsUwyg1KWeCD97I1kjToCiYh4XoGxbtcr62Pr559L55+efWmbhQt9j+lx0Dxo0SO+//74mTpwoh8OhZ555Ru3bt9eiRYvUu3dv3zMAKiJjrNmbTxewjX47orD0TG1rWFWuAKs4brctWc33HlZY+imFpmcq7PRDn460CuCXXspZu/hf/5LeeivfLdXTTvfWxt/3jP6sYt2C3XXDGvVdsyx/TkvmW1+vvDKn6N65UzrTxbSMXD3FIY6cAjbvWsSt+0hRuXqPOnaUnnoqpyDO7kHO/tq2bU7boUOtQTXZBfaZLjlefLE0d27Bx47mWYop1CHVpjcYyK2iz9gdEZl29kaSatWyOREAQKmpXFm6+urijVmkdbr79u2rvn37Fm8mQFmSnp6/Zzc1VS13bVPQqQxtanGBu2nbrRtV5/dEBWeeXos4I0NBmRnS+s+tn1u8OKc3duRIqyjMvQaxpBXZsd69XUcqWT2713y5TTf97+cCkjvdKz5uXE7R/ccfVu9xIYJP5SxIfCy6sg5Ur+WeMCt7XHD7iy+0Ct7KlXN+cMAAq3e6oKWZ3rhEis41A3THYOtRkPjPPLc7drQe3ggL8+ztBmALZuyWatU/oIioFKWmRCr/mG5JMoqMPqG4uDPdgg4A8GezZxd/TJ+L7ieffFI9e/ZUt27dFB7u3W1YgDe86mExxrOnc8+e/OsRp6aq6/rVchij1R1yZobutXa56vz+m3uZpeyHFs6wxu/mnn1/2DBpUf7lRbLnXH5w7FS5To+Zbbttoy7avC7/L7R1o/U1LS2n6M7MtJaJyi0wUMdCnPorJFBBmTnjiLc2rKYvLm6okyGBOhkSqL9CgnQyNFB3xw62itDcY4Nvu03q0yffbdWPvvS2MoKClRWQc6p/fVF3fX1R93zptv9HATNKt23r2eucW1V6iYHywpsZuytC4e10GsX1TTj9Whh5Ft7W/0eX9ElQQEAxTmkLACj3fC66161bp5dfflnp6elq3769evbsqR49euiSSy5RZGSkHTnCTziMS4GZmdZ43MxMa/KpbAkJ1v48vcdXfPW1TgUF6bVq97p7WF7RSLXRJkU6T6haxB+KdKQqKPOUNOVxq9A8cCAn7ogRBY61HS4pPSjYo+husXu7zt9ZwJj37LF5uaejzb6gFBTkUcQmHk9TRlCwArMylXG67dbGLZUaFmGtRRwYpPRga2bpa6/tn7O0UrZx46yJvnL3GAcF6Z/rqkiS+ui/OW1vlb66tYAXukN8/n0tWliPPE6GcmHMbs6gHT607mNbHvCNL2MqpfI9rtLbGbsbNd9dwpkVbOt7tb1u2yre9/hNWu5Sv6Gf5+v1j4w+oUv6VJxef9ind/UXfGh9m215ACjc/PnWsmH79lkjQnNbv973eD4X3YsXL1ZWVpa+++47rVy5UitWrNBrr72mkydPqn379lq7dq3vWcB2DuNSUN4e3h9+sObEvyDnVmnNnSsdOpSvOB7xzQYdj6ykjy+/yt30/ndeUY1DB3NiZp5eMm7yw1YBmLvneORIadOmfHn1k/RnaFUN/usT97622qhuWiO5JKXk+YG8MzLHxFhLJeUZF/zTvt+VERTs0TO+9sLO2tGwea7bqq0ll+69+zrr53L3oM+ZI/3f/+WbOWHS0/kn//r2ws769sLO+fZfe2sBFXNt7z8sAkBJ8HbG7qR9Fef9q0nLXWrUfHeFHt8OABXV9OnSk09aK8f+97/Srbda0xx9/71V0hRFkcZ0BwQEqEuXLqpSpYrOO+88RUVF6eOPP9bOnTuLlgUkY1T9oFFYmlFYmhSWZhSean2v9PlS1arSpZfmtH/6aen48XyzSj+0daeSatTSewP+5m46YeqTqnTieP7nfH2iNfHUt9/m7HvqKWn//nxNL5aUVK2mR9FdKeWYqhwvZGb13MstSdYtylFRnj284eH66sedWv3T6fWNT/ewxCtelXRMaQpXmsJkIhy6/Pplevrvd+T0QGf78MMCn35mAcXxhtaFLGmX+3XNFhpacFsAKGe8nbHb23blhdNpVKdhYmmnAQAoYa+9Zq1Ye/311jzFjz0mNW4sPfOMNaq1KHwuumfMmKGVK1dq5cqVysrKUlxcnHr06KGnn35aF+TuMS2vMjOldevyjSF2f9+sWc6awRkZ1iWSvGsQp6ZqY9oxfX1ZoB58M8IdelPd4yr4Ivowa7xu7uJw+nSr6M6jiSSHPIO4HE6P7YxAa+KsyGpVcpZwyjZ4sHT0aE5hfLoHeeHK9ToeGe3R9K0rR8hpXKd7jHMeUyY9JDk9n7OwGaWn3/SRPv7Bc3rAL5RnFvxUqcpfR6RGjQqMAQAoOm9n7Pa2HQAA/mzfPqnr6T7BsDAp5fSdtzfdJHXufOZVbwvjc9E9cuRIVa9eXQ8//LDuueceRUdHn/2HypO0NOvVLsywYTlFd2Cg9N57BTarLanawVzFscOhtAjJ4ZJOhjv0V5iUFuFQWoRDbcO7SrGxngFGjbJ6k/P0HM/67wqlRHj+m/zr1oeVFRCg9OAQZQYGypwuwl8paOKsQv6Klqfn3/9brXoFvwZ5C+4zoIcF5UlFX24J/snbGbtr1T9QwDEAAMqXmjWt0bYNGliPtWulCy+Udu+2Rq4Whc9F98KFC/X111/rvffe0zPPPKMLL7xQPXv2VM+ePRUXF1f+J1MLD7de/TzFrnu7U6ectk6n9PLL1szVuduGh+uy0AE6UtWzOG18pFKBaxAfDPg6fx7jxxeY3o/bj+bbdyy6si+/YYmih8XS490Z3jfuYF8eKDqWW/IvN7TdU9oplBneztjNBSSgYnrxOh8WLC5gNCPgb3r1shYxat9euv126aGHrInVfvhBGjKkaDF9LrqvuuoqXXXVVZKkY8eOKSEhQfPnz9eVV14ph8Oh9PT0omXiLwIDrWWqvHV/Ab3JkjZlFfDSF1Bwl3f0sKA8YLkl+Dtm7AYAwDJzpuRyWd/fc49UpYq0apU0aJC1XRRFmkjt8OHD7pnLV6xYoZ9//llVq1ZVjx49ipYFKix6WODv/G25JaAwzNgNAIB1s3Lu0bLXXms9zoXPRfcFF1ygLVu2qEqVKurevbvuvPNO9ezZU7F5xxwDXqKHBf6M5ZZQnjBjNwAA1rzS330nHTyY0+udbcQI3+P5XHTfddddFNnF4OA/XGdvlC3etjTKjIrew/LsxhNet+1jYx7wHZMB+qdxXWv41L4IE5XCJv1eutzrtnttzANlx/SXX/K67YPx9uVRXq079aTXbYfqGxszAUrGokXSDTdYC1RFRXmOAHY4Sqjovv/0GOWMjAzt3r1bTZo0UWBgke5SBzzQwwJ/xGSAAAAA5cfDD0u33SZNnGjNgV0cvF/b6bSTJ0/q9ttvV3h4uM4//3zt27dPkvTggw/q+eefL56sAMBPZE8GmD0HQX5GkdEpTAYIAADgBxITpQcfLL6CWypC0f3EE0/oxx9/1IoVKxQaGuref/nll+v9998vvswAwA9kTwZoyVt4MxkgAACAP+nb11oerDj5fF/4xx9/rPfff1+dO3eWI9cN7q1bt9bOnTuLNTmgoui++j8+tL7dtjx8sfU97ycGaxVvXx5lAZMBAgC8VaPOsNJOwScpj9xQ2ikAtvvkk5zvBw6UHn1U2rJFatNGCgrybDt4sO/xfS66//jjD9WokX8CmtTUVI8iHAAqkoo+GSAAAIC/uuqq/PvGj8+/z+GQsrJ8j+/z7eUdO3bU//73v1xPbBXas2bNUpcuXXzPAADKiezJAJvH/qI6DRMpuAEAAPyAy+XdoygFt1SEonvSpEl68sknde+99yozM1PTpk1T7969NWfOHE2YMKFoWQAAAKDCyHI5tWLPJXp30zVasecSZbl8/kgKoAJ57bXX1KhRI4WGhqpDhw5KSEg4+w9JWr16tQIDA9W2bVt7EzwLn9/hunbtqtWrVystLU1NmjTR0qVLFRMTo2+++UYdOnSwI0cAFVRWlrRihfTuu9bXol5dBACUHQu3DlLDaT/r0rf+p+EL39Slb/1PDaf9rIVbB5V2agDKoPfff1+jR4/Wk08+qQ0bNiguLk79+/d3r6JVmGPHjmnEiBG67LLLvHqeb7+VPv/cc9/cuVKjRlKNGtJdd0np6UX7HYq0wHabNm301ltvFe0ZgVLgcjnK9Fhbf5ykpPvuD71u+0cR4i9cKI0aJf32W86+unWladOkIUOKEBAlqqyfc/BfuytPLu0UcA4Wbh2koR/MzbfWQ+LxWhr6wVzNv3aEhrRa5FPM3tVf8KH1bT7FtsOn//2H121vVgGDSoEKZsqUKbr99tt1xx13SJKmTp2qJUuWaMaMGZo0aVKhP3f33Xdr+PDhCggI0Mcff3zW54mPl3r2lPr3t7Y3bZJuv1265RapVSvphRek2rWtdr7yqug+fvy4oqOj3d+fSXY7oKzYua1xvlmlI6JSFNeXWaXLqoULpaFDJZPnU1liorV//nwK77KMcw5AQbJcTo1aPPl0we15s6WRUw65NHrx87qyxf8UUBoJAmVARbponZKS4lFbhoSEKCQkxKNNRkaG1q1bpyeeeMJjf58+fbRmzZpCY8+ePVs7d+7U22+/reeee86rfDZulP6R65rYe+9JnTpJs2ZZ2/XqSePG2Vh0n3feeUpKSlKNGjVUuXLlAmcpN8bI4XAoi/s/UYbs3NZYi+f3z7c/NSVSi+f3V7+hnxfwUyhNWVlWD3fegluy9jkc0ujR0pVXSgF8KitzOOcAFCZhX1f9drxOoceNnNp/vK4S9nVVz5JLCygzKtpF69atW3tsjxs3TvF5Kto///xTWVlZiomJ8dgfExOj5OTkAuP+8ssveuKJJ5SQkKDAQO9v7D5yRMr9NCtXSv365Wx37Cjt3+91OA9eZbF8+XJVqVLF/T1Lg8EfuFwOJSyJO72V92/WIclo1dI4ZWVRvJUlCQmet5TnZYz1hpeQYN0ChLKDcw7AmSSlxJy9kQ/tgPLEm4vW5a3w3rJli+rUybkQl7eXO7e89Wd2h29eWVlZGj58uJ599lk1b97cp3xiYqTdu60e7YwMaf166dlnc46npORfs9tbXhXdPXr0cH/fk0+58BNJ+2p7XCnMz6ETx6Mo3opo2EMHfGgd63XLL256RvJiDNsXNz2jnvsZ61aWcM6hJCzsu9brtkOP97IxE/hqzbKhXre73oe4LWavK1pCpeTJ4Uu8bnvzJzYmgjLD24vWjZrvLuHM7BUVFXXWocnVqlVTQEBAvl7tgwcP5uv9lqxb1n/44Qdt2LBB999/vyTJ5XLJGKPAwEAtXbpUvXoV/H9Dv37SE09IkydLH38shYdLcXE5x3/6SWrSxLffMZvPs5c3btxYTz/9tLZv3160ZwRKSOqJcK/aJSXZnAh8UjXwz2Jth5LDOQfgTJoE/q7KjhNSvmnUshlVdpxQk8DfSzItoNTlXLQu7G5i66J10r7aJZmW1+xcbSY4OFgdOnTQsmXLPPYvW7ZMXbt2zdc+OjpamzZt0saNG92Pe+65Ry1atNDGjRvVqVOnQp/rueesO/F69LDGcc+aJQUH5xx/802pT5+i/R4+z15+//33691339WECRPUrl073XTTTbruuutUq1atomUA2CQiMs2rdvzpli1tIn5U9cDf9UdmdRV8XdCl6kEH1Sbix5JODWfBOQfgTJwOoyER3+rNE71kFd65CwyrEB8S8a2cjvI5aRRQGG8vWnvbriSVxGozY8aM0U033aSLLrpIXbp00cyZM7Vv3z7dc889kqSxY8cqMTFRc+fOldPpVGys5x2WNWrUUGhoaL79eVWvbg1fPHZMiozMPxTuww+t/UXhc0/3mDFj9P3332vbtm264oorNGPGDNWvX199+vTR3Llzi5YFYINa9Q8oIipFZ7qiHhmd4nHbCEpfgMOlkbVekvVhzJXnqEuSQyNrTlWAI+8xlDbOOQBnc2HwXt0WuVyVHake+ys7UnVb5HJdGLy3lDIDSo+3F629bVdSslebyTsXT/ZqMwsXFs/zXHfddZo6darGjx+vtm3b6uuvv9Znn32mBg0aSJKSkpLOuma3LypVKnjumSpVPHu+fVGkdbolqXnz5nr22Wf17LPPau3atbr33nt16623asSIEUUNCRQrp9Morm/C6UkpCr6ifkmfBAUEDCiN9PyeM2iHD629vxfnrffmSZJ6Ljuo756vqrTfc64Nhtd06eLHD2l377u1W3frUh8ygP0451CYirQEDgr3+hMT3N/XcjlVaXdHZR6vocDogwpv9L3WOl3KHrH/oA/rad//31Vet32tSxHvDS1G+8ZeUtopoIzJvmidmhKpgm8xN4qMPqFa9X2ZT8deJb3azH333af77ruvwGNz5sw548/Gx8fnmxW9pBW56Jak7777TvPmzdP777+vY8eOaehQ7ybIAEpKk5a71G/o5/mWX4iMPqFL+pTP5RfKiwa901SvV5oOrgtV2p8BCq+WpRod/pKTWa/LNM455FXRlsCBdxxOlyKafFvaaQBlgrcXrcvSxUpWm/GNz0X3jh079M4772jevHnas2ePLr30Uj3//PMaMmSIoqLONGstUDqatNylRs1308vih5wBUs2L/yrtNOAjzjlkq4hL4ABAUfjbRWtvJ0Vl8lSLz0V3y5YtddFFF2nkyJH629/+ppo1a9qRF1CsnE6jOg0TSzsNoMLgnENFXQIHAIrKny5aezspKpOnWnyeSG3btm367rvvNHr06BIpuBMTE3XjjTeqatWqCg8PV9u2bbVuXc56jMYYxcfHq3bt2goLC1PPnj21efNm2/MCAACF8/clcACgNGRftG4e+4vqNEwskwW3ZK1fXbeuNXa7IA6HVK+emDz1NJ97ups3b66jR49q/vz52rlzpx599FFVqVJF69evV0xMjOrUqVNsyR05ckTdunXTpZdeqs8//1w1atTQzp07VblyZXebf/7zn5oyZYrmzJmj5s2b67nnnlPv3r21fft2bncH/NCcD9t63/j8Y7blARTV9Jdf8rrtg/Hex936nvfFaSsf4trFriVw1p160uu2Q/WNT7Fhr/lv+DDt76PeN+31mg8fZ5/3vqld/G3iN6AgAQHWsmBDh1oFdu4J1bIL8alTi2cStfLA56L7p59+0mWXXabKlStrz549uvPOO1WlShV99NFH2rt3b7EuGzZ58mTVq1dPs2fPdu9r2LCh+3tjjKZOnaonn3xSQ04vBPfWW28pJiZG8+bN0913311suQAAAO/56xI4AADvDBkizZ9f8DrdU6cW3zrd5YHPt5c/9NBDuvXWW/XLL78oNDTUvb9///76+uuvizW5Tz75RBdddJGGDRumGjVqqF27dpo1a5b7+O7du5WcnKw+fXKuAoaEhKhHjx5as2ZNseYCAAC85+267WVpCRwAgG+GDJH27JG++kqaN8/6uns3BXdePhfdP/zwQ4E9yHXq1FFycnKxJJVt165dmjFjhpo1a6YlS5bonnvu0YMPPujuTc9+vpiYGI+fi4mJOWMu6enpOn78uPuRkpJSrHkDAFDRZS+BY8lbeJfNJXAAAL4LCLCWBbv+eusrt5Tn5/Pt5aGhoTp+/Hi+/du3b1f16tWLJalsLpdLF110kSZOnChJateunTZv3qwZM2ZoxIgR7naOPCP4jTH59uU2adIkPfvss8WaK+APXC6HX8yICSC/l6+a5nXb12zMwxf+tgQO7HVd20Fet/3Jh7j+Ns6/0rRx3jd+njHdZcmwH9t53fZDG/OA//G56L7yyis1fvx4ffDBB5Ksgnffvn164okndM011xRrcrVq1VLr1q099rVq1UoLFiyQJPfs6cnJyaqVaz76gwcP5uv9zm3s2LEaM2aMezsxMTHf8wDlzc5tjfN98I2ISlFcXz74ArCPPy2BA6D8ocMBZYHPRfe//vUvDRgwQDVq1NDJkyfVo0cPJScnq0uXLpowYUKxJtetWzdt377dY9+OHTvUoEEDSVKjRo1Us2ZNLVu2TO3aWVeeMjIytHLlSk2ePLnQuCEhIQoJCXFvF9RzD5QnO7c11uL5/fPtT02J1OL5/dVv6OelkBWAioJ12wGUBjocUFb4XHRHR0dr1apVWr58udavXy+Xy6X27dvr8ssvL/bkHnroIXXt2lUTJ07Utddeq++++04zZ87UzJkzJVm97KNHj9bEiRPVrFkzNWvWTBMnTlR4eLiGDx9e7PkA/sjlcihhSfYiiXmHXTgkGa1aGqesLMbgAACA8sGbDgcKb5QUn4ruzMxMhYaGauPGjerVq5d69eplV16SpI4dO+qjjz7S2LFjNX78eDVq1EhTp07VDTfc4G7z2GOP6eTJk7rvvvt05MgRderUSUuXLmWNbpQIf7hlKWlfbY8rvPk5dOJ4lBISrMkvAJyb3tVf8KH1bV63dAbt8CFu+R0HmvLIDWdvhDKpe7P6tsTlbwJ5edvh0Kj5bp/ixsQOLY70UAH5VHQHBgaqQYMGysrKsiuffK644gpdccUVhR53OByKj49XfHx8ieUESP5zy1LqiXCv2iUl2ZwIAABACfC2wyFpX+0SywkVm89Lhj311FMaO3asDh8+bEc+gF/IvmUpNSXSY3/2LUs7tzUupczyi4hM86pdrrkIAQAA/Ja3HQ7etgPOlc9juqdPn65ff/1VtWvXVoMGDRQREeFxfP369cWWHFAW+dsY6Vr1DygiKuX0BYKCltIziow+obg4hmQAAAD/522Hg7ftgHPlc9F91VVX2ZAG4D/8bYy002kU1zfh9GQiRp6FtzX+/JI+CQoIGFAa6QEAABQrbzscatU/UNKpoYLyuegeN26cHXkAfsMfx0g3ablL/YZ+nm8MemT0CV3S59zHoPvDhHJb3/N+3FarePvyQPn36rCRXrd9xcY8AKCi8rbDoax9VkH55XPRDVR0/jpGuknLXWrUfHexF8f+MqEcAACoOOzucAB8QdEN+Mifx0g7nUZ1GiYWWzzWwAQAAGWVXR0OgK98nr0cqOiyb1my5H3Tzj1GukTTKnFnn1BOWrU0Ti5XQRcmAAAA7Jfd4dA89hfVaZhIwY1SQU83UATcsuR/a2D2e+lyr9vutTEPAAAAVCznXHRnZWVp06ZNatCggc4777ziyAnwCxX9liXWwAQAAADOzufby0ePHq033nhDklVw9+jRQ+3bt1e9evW0YsWK4s4PKNMq8i1LrIEJAAAAnJ3PRff8+fN14YUXSpIWLVqk3bt3a9u2bRo9erSefPLJYk8QQNmUPaFc/nHt2Ywio1NYAxMAAAAVms+3l//555+qWbOmJOmzzz7TsGHD1Lx5c91+++2aPn16sScIoGzytzUwd1eeXNopAACglEduKO0UAJQwn3u6Y2JitGXLFmVlZWnx4sW6/HJrcqK0tDQFlPfpmgF4yJ5QLiLqhMf+yOgTLBcGAAAAqAg93bfeequuvfZa1apVSw6HQ71795Ykffvtt2rZsmWxJwigbKvoE8oBAAAAZ+Jz0R0fH6/Y2Fjt379fw4YNU0hIiCQpICBATzzxRLEnCKDsy55QDgAAAIAnn4vu/fv3a+jQofn233zzzVq7dm2xJAUAAAAAQHngc9Hdu3dvrV69WlWrVvXYv3r1ag0cOFBHjx4trtxK3aHMt71uWzXwRhszAQAAAAD4I58nUouLi1OfPn2UkpLi3vf111+rf//+GjduXLEmBwCwl8vlUOKeOtrxczMl7qkjl8tx9h8CAACA13zu6Z45c6aGDRumgQMHaunSpfrmm280ePBgPffccxo1apQdOQIAbLBzW2MlLIlTakqUe19EVIri+iYw8zwAAEAx8bmn2+Fw6N1331VoaKguu+wyDR48WJMmTaLgBgA/snNbYy2e31+pKZEe+1NTIrV4fn/t3Na4lDIDAAAoX7zq6f7pp5/y7Rs3bpyuv/563Xjjjerevbu7zQUXXFC8GZaiqs+N9L5xvG9jure+V9vrtq3ifQoNoAD3/3eV121f69LHxkxKn8vlUMKSuNNbeW8nd0gyWrU0TllZUkBACScHAABQznhVdLdt21YOh0PG5Ky7m739+uuva+bMmTLGyOFwKCsry7ZkAQDnLmlfbY9byvNz6MTxKCUkSD17llRWAAAA5ZNXRffu3bvtzgMAUEJST4R71S4pyeZEAAAAKgCviu4GDRrYnQcAoIRERKZ51a5WLZsTAQAAqAB8nr1cknbu3KmpU6dq69atcjgcatWqlUaNGqUmTZoUd37l1uYDr3rdtpWNeQBlzfSXX/K67YPx3sd1Bu3wIYvyPaa7Vv0DiohKOT2JWkFLhBlFRp9QXNyZbkEHAACAN3yevXzJkiVq3bq1vvvuO11wwQWKjY3Vt99+q/PPP1/Lli2zI0cAQDFyOo3i+iac3jJ5jlrbl/RJYBI1AACAYuBzT/cTTzyhhx56SM8//3y+/Y8//rh69+5dbMkBAOzRpOUu9Rv6eb51uiOjT+iSPqzTDQAAUFx8Lrq3bt2qDz74IN/+2267TVOnTi2OnAAAJaBJy11q1Hy3NZv5iXBFRKapVv0Dcjrz9n4DAACgqHwuuqtXr66NGzeqWbNmHvs3btyoGjVqFFtiAAD7OZ1GdRomlnYaAADARi6Xg4vspcjnovvOO+/UXXfdpV27dqlr165yOBxatWqVJk+erIcfftiOHMulLY8u8qF1L9vyAMqaTRublnYKAFBsDm+r7HXbKi2P2pYHgIpr57bG+YaTRUSlKK4vw8lKis9F99NPP62oqCi9+OKLGjt2rCSpdu3aio+P14MPPljsCQIAAAAAfLdzW2Mtnt8/3/7UlEgtnt9f/YZ+TuFdAnwuuh0Ohx566CE99NBDSklJkSRFRbGsDAAAAACUFS6XQwlL4k5v5V0i1CHJaNXSODVqvruEM6t4irROtyT98ccf2r59uxwOh1q0aKFq1aoVZ14AAAAAgCJK2lfb45by/Bw6cTxKSftql1hOFZXPRXdqaqoeeOABzZ07Vy6XS5IUEBCgESNG6OWXX1Z4eHixJ1keHcxoUtopAGVSyMyfvG/8j+72JQIAxaDKez5MVBRvWxoAKqDUE97VZd62Q9E5ff2BMWPGaOXKlVq0aJGOHj2qo0eP6r///a9WrlzJRGoAAAAAUAZERKYVazsUnc893QsWLND8+fPVs2dP974BAwYoLCxM1157rWbMmFGc+QEAAAAAfFSr/gFFRKUoNSVS+cd0S5JRZPQJ1ap/oKRTq3B87ulOS0tTTExMvv01atRQWhpXSQAAAACgtDmdRnF9E05v5R3qYm1f0ieB9bpLgM9Fd5cuXTRu3Dj99ddf7n0nT57Us88+qy5duhRrcgAAAACAomnScpf6Df1cEVEnPPZHRp9gubAS5PPt5dOmTVO/fv1Ut25dXXjhhXI4HNq4caNCQ0O1ZMkSO3IEAAAAABRBk5a71Kj5bms28xPhiohMU636B+jhLkE+F92xsbH65Zdf9Pbbb2vbtm0yxuhvf/ubbrjhBoWFhdmRIwAAAACgiJxOozoNE0s7jQqrSOt0h4WF6c477yzuXAAAAAAAKFd8LroPHTqkqlWrSpL279+vWbNm6eTJkxo0aJC6d2fNXAAAAAAAsnk9kdqmTZvUsGFD1ahRQy1bttTGjRvVsWNHvfTSS5o5c6Z69eqljz/+2MZUAQAAAADwL14X3Y899pjatGmjlStXqmfPnrriiis0YMAAHTt2TEeOHNHdd9+t559/3s5cAQAAAADwK17fXv79999r+fLluuCCC9S2bVvNnDlT9913n5xOq25/4IEH1LlzZ9sSBQAAAADA33jd03348GHVrFlTkhQZGamIiAhVqVLFffy8885TSkpK8WcIAAAAAICf8rroliSHw3HGbQAAAAAAkMOn2ctvueUWhYSESJL++usv3XPPPYqIiJAkpaenF392AAAAAAD4Ma+L7ptvvtlj+8Ybb8zXZsSIEeeeEQAAAAAA5YTXRffs2bPtzAMAAAAAgHLHpzHdAAAAAADAexTdAAAAAADYhKIbAAAAAACbUHQDAAAAAGATim4AAAAAAGxC0Q0AAAAAgE0ougEAAAAAsAlFNwAAAAAANqHoBgAAAADAJoGlnUBZdv+Ay71u+4qNeQAAAKBkPf3MJ163/cf4wTZmAsDf0dMNAAAAAIBNKLoBAAAAALAJRTcAAAAAADZhTPcZjByxxfvG2+zLAwAAACWr24kHfGjNmG4AhaOnGwAAAAAAm1B0AwAAAABgE4puAAAAAABswpjuM3j5qmlet33NxjwAAABQska8cNzrtgdtzKO8uuGvGV63fSf0XhszAexHTzcAAAAAADah6AYAAAAAwCYU3QAAAAAA2ISiGwAAAAAAmzCR2hn0es2Hl+d5+/IAAABAyTr4D5f3jeNtS6Pcei3B4X3j3vblAZQEeroBAAAAALAJRTcAAAAAADah6AYAAAAAwCZ+NaZ70qRJ+vvf/65Ro0Zp6tSpkiRjjJ599lnNnDlTR44cUadOnfTqq6/q/PPPL91kAQAAABRo/Zi3vG576aZ7bMwEsJ/f9HR///33mjlzpi644AKP/f/85z81ZcoUvfLKK/r+++9Vs2ZN9e7dWykpKaWUKQAAAAAAFr8ouk+cOKEbbrhBs2bN0nnnnefeb4zR1KlT9eSTT2rIkCGKjY3VW2+9pbS0NM2bN68UMwYAAAAAwE+K7pEjR2rgwIG6/PLLPfbv3r1bycnJ6tOnj3tfSEiIevTooTVr1hQaLz09XcePH3c/6BUHAAAAANihzI/pfu+997R+/Xp9//33+Y4lJydLkmJiYjz2x8TEaO/evYXGnDRpkp599tmzPveKUVu8znOoenndFgAAAKjILr3G+8/ZgL8r0z3d+/fv16hRo/T2228rNDS00HYOh8Nj2xiTb19uY8eO1bFjx9yPLVs46QEAAAAAxa9M93SvW7dOBw8eVIcOHdz7srKy9PXXX+uVV17R9u3bJVk93rVq1XK3OXjwYL7e79xCQkIUEhLi3j5+/LgN2QMAAAAAKroy3dN92WWXadOmTdq4caP7cdFFF+mGG27Qxo0b1bhxY9WsWVPLli1z/0xGRoZWrlyprl27lmLmAAAAAACU8Z7uqKgoxcbGeuyLiIhQ1apV3ftHjx6tiRMnqlmzZmrWrJkmTpyo8PBwDR8+vDRSBgAAAADArUwX3d547LHHdPLkSd133306cuSIOnXqpKVLlyoqKqq0UwMAABXc1vdqe922Vbx9eQAASo/fFd0rVqzw2HY4HIqPj1d8fHyp5AMAAAAAQGHK9JhuAAAAAAD8GUU3AAAAAAA28bvbywEAAPzF5gOvet22lY15AABKDz3dAAAAAADYhKIbAAAAAACbUHQDAAAAAGATxnQDAADYZMWoLV63HapeNmYCAMUkvpIPbY/Zl4cfoacbAAAAAACb0NMNAH7A5XIoaV9tpZ4IV0RkmmrVPyCn05R2WgAAADgLim4AKON2bmushCVxSk2Jcu+LiEpRXN8ENWm5qxQzAwAAwNlwezkAlGE7tzXW4vn9lZoS6bE/NSVSi+f3185tjUspMwAAAHiDohsAyiiXy6GEJXGntxx5jlrbq5bGKSurRNMCAACADyi6AaCMStpX+/Qt5XkL7mwOnTgepYSEkswKAAAAvqDoBoAyKvVEuFftkpJsTgQAAABFRtENAGVURGSaV+1q1bI5EQAAABQZs5cDQBlVq/4BRUSlnJ5EraBbzI0io08oLi6qgGMAAADFb+t7tb1u2yrevjz8CT3dAFBGOZ1GcX2zB2znXZPb2r6kT4ICAko0LQAAAPiAohsAyrAmLXep39DPFRF1wmN/ZPQJ9Rv6Oet0AwAAlHHcXg4AZVyTlrvUqPluazbzE+GKiExTrfoH5HTm7f0GAABAWUPRDQB+wOk0qtMwsbTTAACgWEx/+SWv2z4Yb18eQEng9nIAAAAAAGxC0Q0AAAAAgE0ougEAAAAAsAlFNwAAAAAANmEiNQAAAAAlqnf1F3xofZttecB3rw4b6XXbV2zMw5/Q0w0AAAAAKLNee+01NWrUSKGhoerQoYMSEhIKbbtw4UL17t1b1atXV3R0tLp06aIlS5aUYLb5UXQDAAAAAMqk999/X6NHj9aTTz6pDRs2KC4uTv3799e+ffsKbP/111+rd+/e+uyzz7Ru3TpdeumlGjRokDZs2FDCmeeg6AYAAAAAlElTpkzR7bffrjvuuEOtWrXS1KlTVa9ePc2YMaPA9lOnTtVjjz2mjh07qlmzZpo4caKaNWumRYsWlXDmORjTDQAAAOQx/eWXvG77YLx9eZRXtRY8U9opoJSlpKTo+PHj7u2QkBCFhIR4tMnIyNC6dev0xBNPeOzv06eP1qxZ49XzuFwupaSkqEqVKueedBHR0w0AAAAAKFGtW7dWpUqV3I9Jkybla/Pnn38qKytLMTExHvtjYmKUnJzs1fO8+OKLSk1N1bXXXlsseRcFPd0AAAAAgBK1ZcsW1alTx72dt5c7N4fD4bFtjMm3ryDvvvuu4uPj9d///lc1atQoerLniKIbAAAAAFCioqKiFB0dfcY21apVU0BAQL5e7YMHD+br/c7r/fff1+23364PP/xQl19++Tnney4ougEAAIA8WEfaXnc+9brXbT/86HobM0FZFhwcrA4dOmjZsmW6+uqr3fuXLVumK6+8stCfe/fdd3Xbbbfp3Xff1cCBA0si1TOi6AYAAAAAlEljxozRTTfdpIsuukhdunTRzJkztW/fPt1zzz2SpLFjxyoxMVFz586VZBXcI0aM0LRp09S5c2d3L3lYWJgqVapUKr8DRTcAAAAAoEy67rrrdOjQIY0fP15JSUmKjY3VZ599pgYNGkiSkpKSPNbsfv3115WZmamRI0dq5MiR7v0333yz5syZU9LpS6LoBgAAAACUYffdd5/uu+++Ao/lLaRXrFhhf0I+YskwAAAAAABsQk83AAAAkEeL2etKO4Vy7bovnyntFIASQ083AAAAAAA2oegGAAAAAMAmFN0AAAAAANiEMd0AAAAAStS6U0963XaovrExE8B+9HQDAAAAAGATim4AAAAAAGxC0Q0AAAAAgE0Y0w0AAADk8WSvy7xuO+kkY459Ffb3zqWdAlBi6OkGAAAAAMAmFN0AAAAAANiEohsAAAAAAJswphsAAABAiTqY0aS0UwBKDD3dAAAAAADYhKIbAAAAAACbUHQDAAAAAGATim4AAAAAAGzCRGoAAABAHimP3FDaKQAoJ+jpBgAAAADAJhTdAAAAAADYhKIbAAAAAACbUHQDAAAAAGATim4AAAAAAGxC0Q0AAAAAgE0ougEAAAAAsAlFNwAAAAAANqHoBgAAAADAJhTdAAAAAADYhKIbAAAAAACbUHQDAAAAAGATim4AAAAAAGxC0Q0AAAAAgE0ougEAAAAAsAlFNwAAAAAANqHoBgAAAADAJhTdAAAAAADYhKIbAAAAAACbUHQDAAAAAGCTMl10T5o0SR07dlRUVJRq1Kihq666Stu3b/doY4xRfHy8ateurbCwMPXs2VObN28upYwBAAAAAMhRpovulStXauTIkVq7dq2WLVumzMxM9enTR6mpqe42//znPzVlyhS98sor+v7771WzZk317t1bKSkppZg5AAAAAABSYGkncCaLFy/22J49e7Zq1KihdevWqXv37jLGaOrUqXryySc1ZMgQSdJbb72lmJgYzZs3T3fffXdppA0AAAAAgKQy3tOd17FjxyRJVapUkSTt3r1bycnJ6tOnj7tNSEiIevTooTVr1pRKjgAAAAAAZCvTPd25GWM0ZswYXXLJJYqNjZUkJScnS5JiYmI82sbExGjv3r2FxkpPT1d6erp7m1vRAQAAAAB28Jue7vvvv18//fST3n333XzHHA6Hx7YxJt++3CZNmqRKlSq5H61bty72fAEAAAAA8Iui+4EHHtAnn3yir776SnXr1nXvr1mzpqScHu9sBw8ezNf7ndvYsWN17Ngx92PLli32JA4AAAAAqNDKdNFtjNH999+vhQsXavny5WrUqJHH8UaNGqlmzZpatmyZe19GRoZWrlyprl27Fho3JCRE0dHR7kdUVJRtvwMAAAAAoOIq02O6R44cqXnz5um///2voqKi3D3alSpVUlhYmBwOh0aPHq2JEyeqWbNmatasmSZOnKjw8HANHz68lLMHAAAAAFR0ZbronjFjhiSpZ8+eHvtnz56tW265RZL02GOP6eTJk7rvvvt05MgRderUSUuXLqX3GgAAAABQ6sp00W2MOWsbh8Oh+Ph4xcfH258QAAAAAAA+KNNjugEAAAAA8GcU3QAAAAAA2ISiGwAAAAAAm1B0AwAAAABgE4puAAAAAABsQtENAAAAAIBNKLoBAAAAALAJRTcAAAAAADah6AYAAAAAwCYU3QAAAAAA2ISiGwAAAAAAm1B0AwAAAABgE4puAAAAAABsQtENAAAAAIBNKLoBAAAAALAJRTcAAAAAADah6AYAAAAAwCYU3QAAAAAA2ISiGwAAAAAAm1B0AwAAAABgE4puAAAAAABsQtENAAAAAIBNKLoBAAAAALAJRTcAAAAAADah6AYAAAAAwCYU3QAAAAAA2ISiGwAAAAAAm1B0AwAAAABgE4puAAAAAABsQtENAAAAAIBNKLoBAAAAALAJRTcAAAAAADah6AYAAAAAwCYU3QAAAAAA2ISiGwAAAAAAm1B0AwAAAABgE4puAAAAAABsQtENAAAAAIBNKLoBAAAAALAJRTcAAAAAADah6AYAAAAAwCYU3QAAAAAA2ISiGwAAAAAAm1B0AwAAAABgE4puAAAAAABsQtENAAAAAIBNAks7AfiHxX+OKO0UAAAAAMDv0NMNAAAAAIBNKLoBAAAAALAJRTcAAAAAADZhTDe8Eta5mw+tN9mWBwAAAAD4E3q6AQAAAACwCUU3AAAAAAA2oegGAAAAAMAmjOmGV+75Zpr3jW/2vunKpq/7kMX9PrQFAJRHdv2/4W9xAQD+g55uAAAAAABsQtENAAAAAIBNKLoBAAAAALAJY7rLmSkjqpR2CgBQ5vk2zlYqz2NtGXMMlA8TR73tQ2vOZaAk0dMNAAAAAIBNKLoBAAAAALAJRTcAAAAAADah6AYAAAAAwCZMpAav3Plwsi1x7/lmmveNb/a+6eSkIb4nAwDFoNUfrUs7hXLNrv83AH/X7X+pXrfdxLkBlCh6ugEAAAAAsAlFNwAAAAAANqHoBgAAAADAJozpRqmya6x40OMrfGg93JYcgLJoZdPXfWh9f6nHRQ5eY6Bk2TU/A+ey/ZhbA2UNPd0AAAAAANiEohsAAAAAAJtQdAMAAAAAYBPGdAM+YDys/+LfDiiYXeteM6bSwrriKCl2/a3Z9f/c4j9H+BC3bOD/fBQVPd0AAAAAANiEohsAAAAAAJtQdAMAAAAAYBOKbgAAAAAAbFJuJlJ77bXX9MILLygpKUnnn3++pk6dqri4uNJOq1BMMIOS4I9/Z0xSYrHrdbBroh1/myzKp3wl/8u5DOQL5GXX/0n+9v/GNy919r5xGTiX7Xrvqf3kAz5k8ZYPbf2Pv/0Nl0a+vtZ6K1eu1JgxY7R582bVrl1bjz32mO65555iyaUoykVP9/vvv6/Ro0frySef1IYNGxQXF6f+/ftr3759pZ0aAAAAAKCIfK31du/erQEDBiguLk4bNmzQ3//+dz344INasGBBCWeeo1wU3VOmTNHtt9+uO+64Q61atdLUqVNVr149zZgxo7RTAwAAAAAUka+13r///W/Vr19fU6dOVatWrXTHHXfotttu07/+9a8SzjyH399enpGRoXXr1umJJ57w2N+nTx+tWbOmwJ9JT09Xenq6e/vYsWOSpKSkJI92J44f8TqP3377zeu2knTkxB+2xD6VfMjrtkFhxPU1bsbhDK/b+vLvZldcu/7OJPvOD397jf0trl1/E3b+rdnBl3ylsvFaENfeuP52zvkj/u0sqRknvG4bUQbytSvuX0mp3sdV2fic7W//55fVz2rZNdexY8cUHR3t3h8SEqKQkBDP5ypCrffNN9+oT58+Hvv69u2rN954Q6dOnVJQUJDX+Rcb4+cSExONJLN69WqP/RMmTDDNmzcv8GfGjRtnJPHgwYMHDx48ePDgwYMHjzLwGDduXLHUes2aNTMTJkzw2Ld69WojyRw4cMCHSrP4+H1PdzaHw+GxbYzJty/b2LFjNWbMGPd2Zmamtm7dqnr16snpPPMd9ykpKWrdurW2bNmiqKioc0+cuCUS187YxCVuScS1MzZxievPce2MTVzilkRcO2MTl7glEdfX2C6XS/v27VPr1q0VGJhTjubt5c7Nl1qvsPYF7S8pfl90V6tWTQEBAUpOTvbYf/DgQcXExBT4MwXdutCtWzevnu/48eOSpDp16njcDnGuiGtvXDtjE5e4JRHXztjEJa4/x7UzNnGJWxJx7YxNXOKWRNyixK5fv75XcYtS69WsWbPA9oGBgapatapXz1vc/H4iteDgYHXo0EHLli3z2L9s2TJ17dq1lLICAAAAAJyLotR6Xbp0ydd+6dKluuiii0pnPLfKQdEtSWPGjNF//vMfvfnmm9q6daseeugh7du3r1TXYgMAAAAAnJuz1Xpjx47ViBEj3O3vuece7d27V2PGjNHWrVv15ptv6o033tAjjzxSWr+C/99eLknXXXedDh06pPHjxyspKUmxsbH67LPP1KBBg2J/rpCQEI0bN+6MYw6IW/bi2hmbuMQtibh2xiYucf05rp2xiUvckohrZ2ziErck4tod+2y1XlJSksea3Y0aNdJnn32mhx56SK+++qpq166t6dOn65prrin23LzlMNmjygEAAAAAQLEqF7eXAwAAAABQFlF0AwAAAABgE4puAAAAAABsQtENAAAAAIBNKLoBAAAAALBJuVgyDABQvv3yyy9as2aNkpOT5XA4FBMTo65du6pZs2a2PF9qaqrWrVun7t272xK/qLKyshQQEODe/vbbb5Wenq4uXbooKCio2J7n1ltv1YQJE1S7du1ii3nkyBH9+uuvqlWrlurWrVtscY8ePaoPP/xQ+/btU4MGDTRs2DBVqlTJ5zjr1q1Thw4dii2v3A4ePKjNmzerQ4cOio6O1u+//6633npLLpdLAwcOVJs2bYoce9euXVq1apWSkpIUEBCgRo0aqXfv3oqOjj6nnDnnLCV1zkn+c95xznHOoQgMiuSWW24xiYmJxRrz8OHD5rvvvjP79+8vtphHjhwxM2fONE899ZSZNWuWOXr0aJHi/PDDD8WWU16///67Wb58uTl27Jgxxpjk5GQzefJkM2nSJPPTTz+dU+ydO3eat956yzz//PPmhRdeMPPnz3c/z7nYsWOHmTNnjnn++efN5MmTzZw5c8yOHTvOOW5hTpw4YVauXGlb/HORmZnpsb127VqzcuVKk5GRUazPwzlXfPzpnDt69KgZPHiwcTgcpnLlyqZ58+amWbNmpnLlysbpdJorr7yyWM7pvDZu3GicTqfPP5eRkWEeffRR06RJE9OxY0fz5ptvehxPTk4uUtwDBw6Ybt26mYCAANO9e3dz+PBhM3DgQONwOIzD4TDNmzc3Bw4c8Dnujz/+WOAjKCjIfPTRR+5tX40dO9akpqYaY6zX5M477zROp9M4HA7jdDrN1VdfbU6ePOlzXGOMueaaa8yCBQuMMcZs3rzZVKtWzVSvXt106tTJxMTEmJo1a5otW7b4HNfhcJjGjRubCRMmmN9++61IuRXkq6++MhEREcbhcJhatWqZH3/80dStW9c0a9bMtGjRwoSEhJglS5b4HPfEiRNm6NCh7r8Bp9NpatasaQICAkxkZKR55ZVXipQv55zFrnPOGP877zjnLJxzOew678oziu6z4I3Rwhujxd/eGO18U6QIsHDOWew652666SbTpk0bs3bt2nzH1q5day644AIzYsSIIsU+k6Kec+PGjTMxMTHmhRdeME8++aSpVKmSueuuu9zHk5OTjcPh8DnuTTfdZLp27Wo++eQTc91115muXbuauLg489tvv5l9+/aZuLg4M3LkSJ/jZv9bZf+75X7kPj985XQ6ze+//26MMWbChAmmevXqZsGCBSYxMdEsWrTI1KlTx4wfP97nuMYYU61aNfdFzv79+5vhw4eb9PR0Y4x1bt9+++2mT58+Psd1OBzmzjvvNDExMSYwMNAMHDjQfPTRR/kuLPqqW7duZuTIkSYlJcW88MILpm7duh7/Vo888ojp2rWrz3Hvuusu061bN7Nx40azbds2c80115jHHnvMpKammjfeeMOEh4ebd955x+e4nHMWu845Y/zvvOOcs3DO5bDrvCvPKLrPgjdGC2+MFn97Y7TzTZEiwMI5Z7HrnKtUqVKB51u2b775xlSqVMnnuOedd94ZH9HR0UX6O2vatKlZtGiRe/vXX381zZo1M7fccotxuVxFvtBVq1Yt88033xhjjDl06JBxOBzmiy++cB9fvny5ady4sc9xL7zwQjNw4ECzdetWs2fPHrNnzx6ze/duExgYaJYtW+be5yuHw+E+39q2bWveeOMNj+Pvv/++adWqlc9xjTEmLCzM/Prrr8YY63VZv369x/Ht27cX6W8iO+dTp06Z+fPnmwEDBpiAgAATExNjHnvsMbNt27Yi5RsdHe3O99SpUyYwMNBs2LDBfXzHjh1FyrdatWoed8QcPnzYhIaGui8uvvLKK6Zt27Y+x+Wcs9h1zhnjf+cd55yFcy6HXeddeUbRfRa8MXrmyxujf70x2vmmSBFg4Zyz2HnOffvtt4UeX7t2bZHyDQ8PNw8//LCZM2dOgY9nn322SOdGWFiY2b17t8e+xMRE06JFC3PDDTeYxMTEIsUNDQ01+/btc29HRESYX375xb29d+9eExYW5nPc9PR0M2rUKNO6dWuPv93AwECzefNmn+Nlczgc5uDBg8YYY6pWrWo2bdrkcXz37t0mPDy8SLE7depkZs6caYwxpl27duajjz7yOL506VJTs2bNIuWc/R6R7bfffjPjx483jRs3Nk6n08TFxfkct1q1aubnn382xhiTmppqnE6n+73TGOvunmrVqvkct3Llyh7DmjIyMkxgYKD7dd+xY4cJDQ31OS7nnMWuc84Y/zvvOOcsnHM57DrvyjOK7rPgjTEnX94Y/e+N0c43RYoAC+ecxa5z7sYbbzQXXHCB+f777/Md+/77703btm3NTTfd5HPcrl27mqlTpxZ6vKh3lzRq1Mjj4lO2xMRE07x5c3P55ZcXKW79+vU93nsef/xxc+jQIY98i/Lvlu2zzz4zdevWNRMnTjRZWVnFcr5NmDDBTJs2zdSuXdt8/fXXHsc3btxozjvvvCLF/vTTT02VKlXM7NmzzezZs03Dhg3Nf/7zH7N69Wrz5ptvmnr16plHH33U57i574YpyBdffGGGDx/uc9wrr7zSXHHFFWbVqlXmrrvuMhdddJEZOHCgOXHihElNTTVDhw41/fr18zlu7969Pe5SeeGFF0ytWrXc2+vXry/S3wTnnMXuc84Y/znvOOcsnHM57DrvyjOKbi/xxsgbozH+98Zo55siRYCFc85i1zl35MgR069fP+NwOMx5551nWrRoYVq2bGnOO+8843Q6Tf/+/c2RI0d8jjthwgQTHx9f6PF9+/aZW265xee4t99+u7ntttsKPPbbb7+Zpk2bFumcGzx48BnfI1555RXTq1cvn+PmlpycbPr3728uueSScz7fGjRoYBo2bOh+5M39pZdeMp07dy5y/Pnz55u6devmG4oSGhpqRo8eXaRhGAVd6CoOO3bsME2bNjUOh8Ocf/75JjEx0QwePNgEBgaawMBAU716dbNu3Tqf465bt85UqVLF1KxZ09SvX98EBwebd9991338lVdeKdJwJ845S0mcc8b4z3nHOcc5l5td5115RtHtA94YeWP0tzdGO98UKQJycM7Zd85l27Jli3nzzTfNxIkTzcSJE82bb75ptm7dWuR4dtmzZ49ZvHhxoccPHDhg5syZU+zP+9133+W7e6Oopk2bZq666qpindU/r2+++SbfUAxfZWZmmm+//da89957Zt68eearr74yx48fL3K8FStWmFOnTp1TTmfy559/emx/8cUXZtGiRfn2++LAgQNm5syZ5uWXXz6n98eCbN26lXPuDIrznDPGP867zMxM891333HOVfBzzpjSO+/8mcMYY0p72TJ/M336dH311Vd6+eWXi3Wt0dzWrl2rkJAQtWvXrkg/n5WVpfXr12vXrl1yuVyqVauWOnTooKioqCLFW7lypbp166bAQHuWdj906JCqVq3q3v7yyy918uRJdenSxWO/L5KSkvTpp58qPT1dvXr1UuvWrYsrXW3dulVr165VcnKyJKlmzZrq0qWLWrZsWWzPURz27t2rbdu2qW/fvgUeT0pK0tKlS3XzzTcX+3N///33CgsLU2xs7DnH8pdzbt26ddq9ezfnnA3nHAAAgL+i6AYAlGnGGH3xxRdas2aNkpOT5XA4FBMTo27duumyyy6Tw+EgbgWKe6bYXbt21eWXX17mcva3uGdy5MgRLVq0SCNGjCCuDXHtjE3csh3X5XLJ6XQWuP+3335T/fr1y1Rcu2OXNxTdXvjxxx+1fv169ezZU40aNdLmzZv16quvyuVy6eqrry60F5G4ZSPupZdeqoYNGxZbXElavny5Vq1apaSkJAUEBKhRo0YaPHiwmjVrVuSY/hi3JHNu3LixBg0aVGZfC+LaEzcxMVFXXHGFNm3apNjYWMXExMgYo4MHD+rnn3/WhRdeqE8++UR16tQhbgWI6485+1vcs/nxxx/Vvn17ZWVlEdeGuHbGJm7ZjHv8+HHdcccdWrRokaKjo3XPPffomWeeUUBAgCTp999/V+3atctMXLtjl1sldR+7v5o/f74JCAgwVatWNVFRUeaLL74wlStXNpdffrnp27evCQgIKNLas8T1z7i///67ufjii43D4TABAQHG6XSaDh06mJo1a5qAgIAiTZjlj3H9MWfi+mfcwYMHm169epkDBw7kO3bgwAHTq1cvc+WVVxK3gsS1MzZxLceOHTvjIyEhoUhzgRDXf3Mmrr1xH3zwQdO8eXPz4YcfmlmzZpkGDRqYgQMHmvT0dGOMNb+Nw+EoM3Htjl1eUXSfRfv27c1zzz1njDHm3XffNZUrVzbjx493H//Xv/5VpLVnieufca+77jpz1VVXmSNHjpi0tDQzcuRI9+RQX375palateoZJxcrL3H9MWfi+mfciIgIs3HjxkKPr1+/3kRERBC3gsS1MzZxLQ6HwzidzkIf2ceJW7S4/pgzce2NW79+ffPVV1+5t//880/TqVMn06dPH/PXX3+Z5OTkMhXX7tjlFUX3WURERLjXOXa5XCYoKMj89NNP7uM7d+40kZGRxK0gcaOjo91rHBtjzIkTJ0xQUJA5duyYMcaY//u//zMtWrQo93H9MWfi+mfcatWqmeXLlxd6/MsvvyzSUmTE9c+4dsYmriU6OtpMnjzZrFixosDHrFmzivRhmrj+mzNx7Y0bHh5udu3a5bHv+PHjpkuXLqZXr15m165dZSqu3bHLK3umxS1HoqKidOjQITVs2FBHjx5VZmamDh065D5+6NAhRUZGEreCxA0JCfGYmMbpdCorK0uZmZmSpK5du2rPnj3lPq4/5kxc/4z7t7/9TTfffLOmTJmi3r17q1KlSpKkY8eOadmyZXr44Yc1fPhw4laQuP6Ys7/Fbd++vSSpR48eBR6vXLmyTBGmAyKu/bGJ659x69Wrp61bt6pRo0bufVFRUVq6dKn69Omjq6++2ueYdsa1O3a5VVrVvr+48cYbTadOnczbb79tBg0aZPr162c6d+5stm7darZt22Z69Ohhhg4dStwKEvfqq68211xzjTlx4oTJyMgwo0ePNk2bNnUfX7t2ralZs2a5j+uPORPXP+Omp6ebe+65xwQHBxun02lCQ0NNaGiocTqdJjg42Nx7773uMWTELf9x/TFnf4s7c+ZMM23atEKPJycnm/j4eOIWMa6dsYnrn3EfeOCBQj+THj9+3HTq1KlIvcZ2xbU7dnlF0X0WycnJ5vLLLzeRkZGmf//+5tixY+b+++93j9to1qyZ+fXXX4lbQeLu3LnTNGnSxAQGBpqgoCBTuXJls2zZMvfx2bNnmyeeeKLcx/XHnInrn3GzHTt2zHz55Zdm3rx5Zt68eWb58uXuW9fPBXH9M66dsYkLoCQdPnzYY3hWXikpKWbFihVlJq7dscsrlgwrol27diktLU0tW7ZUYGDx3aVP3LIfNy0tTatWrVJGRoY6d+6satWqFUtu/hbXztjEJS4AAEB5QdENACjTUlNTNW/ePK1Zs0bJyclyOByKiYlRt27ddP311ysiIoK4FSiuP+ZMXOL6e87EJW5Jxi6PKLq9cPLkSb377rtatWqVkpKSFBAQoEaNGumqq67SZZddRlziVsi4/pgzcf0v7pYtW9S7d2+lpaWpR48eiomJkTFGBw8e1MqVKxUREaGlS5eqdevWxK0Acf0xZ+IS199zJi5xSzJ2uVUiN7H7sV9++cU0aNDAVK1a1dSqVcs4HA4zcOBA06lTJxMQEGCGDRtmTp06RVziVqi4/pgzcf0zbs+ePc3f/va3AieESk9PN9dff73p2bMncStIXH/MmbjE9feciUvckoxdXlF0n0X//v3N3XffbbKysowxxkyaNMn079/fGGPMjh07TMOGDc24ceOIS9wKFdcfcyauf8YNCwszmzdvLvT4pk2bTFhYGHErSFw7YxOXuCUR187YxCVuScS1O3Z5RdF9FuHh4WbHjh3u7fT0dBMUFGT+/PNPY4wxH3/8sWnYsCFxiVuh4vpjzsT1z7i1a9c2H3/8caHHP/roI1O7dm3iVpC4dsYmLnFLIq6dsYlL3JKIa3fs8oqi+yxq165t1q1b594+cuSIcTgc5vjx48YYY3bt2mVCQkKIS9wKFdcfcyauf8YdN26cqVSpknnhhRfMxo0bTVJSkklOTjYbN240L7zwgjnvvPPMs88+S9wKEtcfcyYucf09Z+IStyRjl1cU3Wdx8803mx49epitW7eaXbt2meuuu860a9fOfXzFihWmXr16xCVuhYrrjzkT1z/jGmPM888/7x4n7nQ6jdPpNA6Hw9SqVctMnjy5SDGJ679x/TFn4hLX33MmLnFLMnZ5RNF9Fr///rvp3Lmz+w+qYcOGZv369e7jH374oZk+fTpxiVuh4vpjzsT1z7i57dq1y6xZs8asWbPG7Nq165xiEdf/49oZm7jELYm4dsYmLnFLIq7dscsTlgzz0i+//KL09HS1bNlSgYGBxCUucW2OTVziAgAAlAulXfX7u3379plbb72VuMQlbgnFJm7Fi5uWlmYSEhIKnCn15MmT5q233iJuBYprZ2ziErck4toZm7jELYm4dscujyi6z9HGjRuN0+kkLnGJW0KxiVux4m7fvt00aNDAfdt6jx49zIEDB9zHk5OTiVuB4vpjzsQlrr/nTFzilmTs8op7/87ik08+OePxXbt2EZe4FS6unbGJS9zcHn/8cbVp00Y//PCDjh49qjFjxqhbt25asWKF6tevX6SYxPXfuP6YM3GJ6+85E5e4JRm73Crtqr+sy76C43A4Cn0U5UoOcYnrz3H9MWfi+mfcGjVqmJ9++slj33333Wfq169vdu7cWeSr6cT1z7j+mDNxievvOROXuCUZu7yi6D6L2rVrm48++qjQ4xs2bCjSHxVxievPce2MTVzi5hYVFWW2bNmSb//9999v6tata77++mviVqC4/pgzcYnr7zkTl7glGbu8cpZ2T3tZ16FDB61fv77Q4w6HQ6YIE8ATl7j+HNfO2MQlbm4tW7bUDz/8kG//yy+/rCuvvFKDBw/2OSZx/TeunbGJS9ySiGtnbOIStyTi2h273Cr5Ot+/fP311+bzzz8v9PiJEyfMihUriEvcChXXztjEJW5uEydONP379y/0+L333mscDgdxK0hcO2MTl7glEdfO2MQlbknEtTt2ecU63QAAAAAA2ITbywEAAAAAsAlFNwAAAAAANqHoBgAAAADAJhTdAAAAAADYhKIbAOAX4uPj1bZt29JOA2cxZ84cVa5cubTT0IoVK+RwOHT06NHSTgUAUMFRdAMA/MIjjzyiL7/8srTTQBnUs2dPjR492mNf165dlZSUpEqVKpVOUgAAnEbRDQCwXUZGxjnHiIyMVNWqVYshG/+QlZUll8tV2mnkc+rUqdJOwSvBwcGqWbOmHA5HaacCAKjgKLoBAMWuZ8+euv/++zVmzBhVq1ZNvXv31pYtWzRgwABFRkYqJiZGN910k/78809J0uuvv646derkKzIHDx6sm2++WVLBt5fPnj1brVq1UmhoqFq2bKnXXnvNfeyaa67RAw884N4ePXq0HA6HNm/eLEnKzMxUVFSUlixZcsbfZe7cuapatarS09M99l9zzTUaMWKEe3vRokXq0KGDQkND1bhxYz377LPKzMx0H58yZYratGmjiIgI1atXT/fdd59OnDjhPp59W/ann36q1q1bKyQkRHv37tWKFSt08cUXKyIiQpUrV1a3bt20d+/eM+ac+/V6/fXXVa9ePYWHh2vYsGH5brc+02u4Z88eORwOffDBB+rZs6dCQ0P19ttvn/W585oxY4aaNGmi4OBgtWjRQv/3f//ncfzo0aO66667FBMTo9DQUMXGxurTTz+VJB06dEjXX3+96tatq/DwcLVp00bvvvuu+2dvueUWrVy5UtOmTZPD4ZDD4dCePXsKvL18wYIFOv/88xUSEqKGDRvqxRdf9MijYcOGmjhxom677TZFRUWpfv36mjlzps+/LwAAHgwAAMWsR48eJjIy0jz66KNm27ZtZs2aNaZatWpm7NixZuvWrWb9+vWmd+/e5tJLLzXGGHPo0CETHBxsvvjiC3eMw4cPm+DgYLNkyRJjjDHjxo0zF154ofv4zJkzTa1atcyCBQvMrl27zIIFC0yVKlXMnDlzjDHGTJ8+3cTGxrrbt23b1lSrVs28+uqrxhhj1qxZYwIDA01KSsoZf5e0tDRTqVIl88EHH7j3/fHHHyY4ONgsX77cGGPM4sWLTXR0tJkzZ47ZuXOnWbp0qWnYsKGJj493/8xLL71kli9fbnbt2mW+/PJL06JFC3Pvvfe6j8+ePdsEBQWZrl27mtWrV5tt27aZo0ePmkqVKplHHnnE/Prrr2bLli1mzpw5Zu/evWf9Nxg3bpyJiIgwvXr1Mhs2bDArV640TZs2NcOHD/f6Ndy9e7eRZBo2bOhuk5iYeMbnnT17tqlUqZJ7e+HChSYoKMi8+uqrZvv27ebFF180AQEB7tcuKyvLdO7c2Zx//vlm6dKlZufOnWbRokXms88+M8YY89tvv5kXXnjBbNiwwezcudNMnz7dBAQEmLVr1xpjjDl69Kjp0qWLufPOO01SUpJJSkoymZmZ5quvvjKSzJEjR4wxxvzwww/G6XSa8ePHm+3bt5vZs2ebsLAwM3v2bHeuDRo0MFWqVDGvvvqq+eWXX8ykSZOM0+k0W7duPevrDQBAYSi6AQDFrkePHqZt27bu7aefftr06dPHo83+/fuNJLN9+3ZjjDGDBw82t912m/v466+/bmrWrGkyMzONMfmL7nr16pl58+Z5xPzHP/5hunTpYowx5qeffjIOh8P88ccf5vDhwyYoKMg899xzZtiwYcYYYyZOnGg6derk1e9z7733mv79+7u3p06daho3bmxcLpcxxpi4uDgzceJEj5/5v//7P1OrVq1CY37wwQematWq7u3Zs2cbSWbjxo3ufYcOHTKSzIoVK7zKM7dx48aZgIAAs3//fve+zz//3DidTpOUlGSMOftrmF10T5061evnzVt0d+3a1dx5550ebYYNG2YGDBhgjDFmyZIlxul0uv8OvDFgwADz8MMPu7d79OhhRo0a5dEmb9E9fPhw07t3b482jz76qGndurV7u0GDBubGG290b7tcLlOjRg0zY8YMr3MDACAvbi8HANjioosucn+/bt06ffXVV4qMjHQ/WrZsKUnauXOnJOmGG27QggUL3Ldxv/POO/rb3/6mgICAfLH/+OMP7d+/X7fffrtHzOeee84dLzY2VlWrVtXKlSuVkJCgCy+8UIMHD9bKlSslWbNb9+jRw6vf5c4779TSpUuVmJgoybol+5ZbbnGPF163bp3Gjx/vkcudd96ppKQkpaWlSZK++uor9e7dW3Xq1FFUVJRGjBihQ4cOKTU11f08wcHBuuCCC9zbVapU0S233KK+fftq0KBBmjZtmpKSkrzKWZLq16+vunXrure7dOkil8ul7du3e/UaZsv9b+mrrVu3qlu3bh77unXrpq1bt0qSNm7cqLp166p58+YF/nxWVpYmTJigCy64QFWrVlVkZKSWLl2qffv2FUsev/zyi7Kystz7cr/+DodDNWvW1MGDB316LgAAcgss7QQAAOVTRESE+3uXy6VBgwZp8uTJ+drVqlVLkjRo0CC5XC7973//U8eOHZWQkKApU6YUGDt77PesWbPUqVMnj2PZRbrD4VD37t21YsUKBQcHq2fPnoqNjVVWVpY2bdqkNWvW5JvxujDt2rXThRdeqLlz56pv377atGmTFi1a5JHPs88+qyFDhuT72dDQUO3du1cDBgzQPffco3/84x+qUqWKVq1apdtvv91jYrKwsLB8E3/Nnj1bDz74oBYvXqz3339fTz31lJYtW6bOnTt7lXtu2bEdDodXr2G23P+WRZH3dzLGuPeFhYWd8WdffPFFvfTSS5o6dap7TPzo0aN9npwv93Pm3pdXUFBQvtzL4oR2AAD/QdENALBd+/bttWDBAjVs2FCBgQX/1xMWFqYhQ4bonXfe0a+//qrmzZurQ4cOBbaNiYlRnTp1tGvXLt1www2FPm/Pnj01c+ZMBQcHa/z48XI4HIqLi9O//vUvnTx5Ml/P55nccccdeumll5SYmKjLL79c9erV8/j9tm/frqZNmxb4sz/88IMyMzP14osvyum0bjL74IMPvH7udu3aqV27dho7dqy6dOmiefPmeVV079u3TwcOHFDt2rUlSd98842cTqeaN2/u9Wt4rlq1aqVVq1Z5TDq3Zs0atWrVSpLVs/zbb79px44dBfZ2JyQk6Morr9SNN94oybrA8csvv7h/XrLuEMjdW12Q1q1ba9WqVR771qxZo+bNmxd4NwUAAMWFohsAYLuRI0dq1qxZuv766/Xoo4+qWrVq+vXXX/Xee+9p1qxZ7qLnhhtu0KBBg7R582Z3kVWY+Ph4Pfjgg4qOjlb//v2Vnp6uH374QUeOHNGYMWMkWUX3qFGjFBgYqLi4OPe+hx9+WO3bt1d0dLTXv8MNN9ygRx55RLNmzdLcuXM9jj3zzDO64oorVK9ePQ0bNkxOp1M//fSTNm3apOeee05NmjRRZmamXn75ZQ0aNEirV6/Wv//977M+5+7duzVz5kwNHjxYtWvX1vbt27Vjxw6PAvZMQkNDdfPNN+tf//qXjh8/rgcffFDXXnutatas6fVreK4effRRXXvttWrfvr0uu+wyLVq0SAsXLtQXX3whSerRo4e6d++ua665RlOmTFHTpk21bds2ORwO9evXT02bNtWCBQu0Zs0anXfeeZoyZYqSk5M9iu6GDRvq22+/1Z49exQZGakqVarky+Phhx9Wx44d9Y9//EPXXXedvvnmG73yyises7UDAGAHxnQDAGxXu3ZtrV69WllZWerbt69iY2M1atQoVapUyd3zK0m9evVSlSpVtH37dg0fPvyMMe+44w795z//0Zw5c9SmTRv16NFDc+bMUaNGjdxtYmNjVa1aNV144YXuArtHjx7Kysryejx3tujoaF1zzTWKjIzUVVdd5XGsb9+++vTTT7Vs2TJ17NhRnTt31pQpU9SgQQNJUtu2bTVlyhRNnjxZsbGxeueddzRp0qSzPmd4eLi2bduma665Rs2bN9ddd92l+++/X3fffbdXOTdt2lRDhgzRgAED1KdPH8XGxnoUmd68hufqqquu0rRp0/TCCy/o/PPP1+uvv67Zs2erZ8+e7jYLFixQx44ddf3116t169Z67LHH3D3XTz/9tNq3b6++ffuqZ8+eqlmzZr7X/5FHHlFAQIBat26t6tWrFzjeu3379vrggw/03nvvKTY2Vs8884zGjx+vW265pdh+VwAACuIwBQ1oAgAA+fTu3VutWrXS9OnTSzuVs4qPj9fHH3+sjRs3lnYqAABUaNxeDgDAWRw+fFhLly7V8uXL9corr5R2OgAAwI9QdAMAKrR9+/apdevWhR7fsmWLunfvriNHjmjy5Mlq0aJFCWZXuPPPP1979+4t8Njrr79u2/P2799fCQkJBR77+9//rr///e+2PTcAAP6I28sBABVaZmam9uzZU+jxM824Xpr27t3rsdxYbjExMYqKirLleRMTE3Xy5MkCj1WpUqXAScwAAKjIKLoBAAAAALAJs5cDAAAAAGATim4AAAAAAGxC0Q0AAAAAgE0ougEAAAAAsAlFNwAAAAAANqHoBgAAAADAJhTdAAAAAADYhKIbAAAAAACb/D8d+6B6fgBTGgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a figure and subplots\n",
    "fig, ax1 = plt.subplots(figsize=(10, 8))\n",
    "\n",
    "# Plot 1: Loctions counts and Year with different colors for each group\n",
    "\n",
    "\n",
    "ax1.set_xticks(range(0, len(locations_yearly_counts.index), 5))\n",
    "ax1.set_xticklabels(locations_yearly_counts.index[::5])\n",
    "ax1.set_xlabel('Year')\n",
    "ax1.set_ylabel('Books reviewed per Place of Publication', color='black')\n",
    "ax1.set_title('Bookcounts by Place of Publication and Year')\n",
    "# Create legend elements based on location_color_dict\n",
    "\n",
    "# Loop through each location\n",
    "# Plotting\n",
    "locations_yearly_counts.plot(kind='bar', stacked=True,  ax=ax1,color=[location_color_dict[col] for col in locations_yearly_counts.columns])\n",
    "\n",
    "\n",
    "legend_elements = []\n",
    "for location, color in top_location_color_dict.items():\n",
    "    legend_elements.append(plt.Line2D([0], [0],  color= color, label=location, markerfacecolor=color, markersize=10))\n",
    "\n",
    "# Plot the legend\n",
    "ax1.legend(handles=legend_elements, loc='upper left')\n",
    "\n",
    "\n",
    "# Plot 2: Shannon Equitability with blue dots\n",
    "ax2 = ax1.twinx()\n",
    "shannon_equitability_locations.plot(kind='line', ax=ax2, marker='o', color='blue', linestyle='', label='Yearly SEI Values')\n",
    "ax2.set_ylabel('Shannon Equitability', color='blue')\n",
    "\n",
    "# Calculate and plot the linear regression line\n",
    "\n",
    "x_values = np.arange(len(shannon_equitability_locations_cleaned))\n",
    "x_values_extended = np.linspace(min(x_values), max(x_values) + 3, 100)  # Extend the range by 3 units\n",
    "# Calculate the corresponding y values using the linear regression equation\n",
    "y_values_extended = slope * (x_values_extended + 1984) + intercept\n",
    "\n",
    "# Plot the line on the second axis\n",
    "ax2.set_ylim(0, 1)\n",
    "ax2.plot(x_values_extended, y_values_extended, 'r--', label='Linear Regression: SEI for Locations')\n",
    "# Add a legend to the plot\n",
    "ax2.legend(loc='upper right', bbox_to_anchor=(0.7, 0.99))\n",
    "\n",
    "\n",
    "# Adjust layout and show the combined plot\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "55.0"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "locations_yearly_counts.loc[\"1984\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "counts_books_published_per_year = df_location_publishers_exploded.groupby('review_years_per_location')['location_publisher'].value_counts()\n",
    "shannon_diversity_locations = counts_books_published_per_year.groupby(level='review_years_per_location').apply(lambda x: shannon_diversity_index(x))\n",
    "shannon_diversity_locations.loc[\"2000\"]= np.nan\n",
    "shannon_diversity_locations.loc[\"2009\"]= np.nan\n",
    "shannon_diversity_locations.loc[\"2010\"]= np.nan\n",
    "shannon_diversity_locations= shannon_diversity_locations.sort_index()\n",
    "\n",
    "# plot the yearly entropy values\n",
    "shannon_diversity_locations.plot(kind='bar', title='Shannon Diversity: location of publication')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    370.000000\n",
       "mean       8.808108\n",
       "std       20.047327\n",
       "min        1.000000\n",
       "25%        1.000000\n",
       "50%        1.000000\n",
       "75%        3.000000\n",
       "max      105.000000\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts_books_published_per_year.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "review_years_per_location  location_publisher\n",
       "1984                       bogota                40\n",
       "                           medellin               9\n",
       "                           buenos aires           2\n",
       "                           santa rosa de osos     1\n",
       "                           managua                1\n",
       "                                                 ..\n",
       "2021                       santa marta            1\n",
       "2022                       bogota                32\n",
       "                           madrid                 2\n",
       "                           medellin               2\n",
       "                           barranquilla           1\n",
       "Name: count, Length: 370, dtype: int64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts_books_published_per_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "review_years_per_location\n",
       "1984    1.355788\n",
       "1985    1.326390\n",
       "1986    1.579550\n",
       "1987    1.914209\n",
       "1988    1.692474\n",
       "1989    1.466360\n",
       "1990    1.213044\n",
       "1991    1.768758\n",
       "1992    1.039659\n",
       "1993    1.074767\n",
       "1994    1.117077\n",
       "1995    1.536858\n",
       "1996    1.077918\n",
       "1997    0.576219\n",
       "1998    1.071458\n",
       "1999    1.368668\n",
       "2000         NaN\n",
       "2001    1.626979\n",
       "2002    1.579837\n",
       "2003    1.638915\n",
       "2004    1.528795\n",
       "2005    1.329345\n",
       "2006    2.094384\n",
       "2007    1.445621\n",
       "2008    1.727370\n",
       "2009         NaN\n",
       "2010         NaN\n",
       "2011    1.378647\n",
       "2012    1.603057\n",
       "2013    2.247051\n",
       "2014    2.204328\n",
       "2015    2.099864\n",
       "2016    1.826473\n",
       "2017    2.365656\n",
       "2018    1.228192\n",
       "2019    1.363242\n",
       "2020    1.309596\n",
       "2021    0.900749\n",
       "2022    0.777021\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shannon_diversity_locations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Dickey-Fuller Test for Dependent Variable Entropy Publishers:\n",
      "ADF Statistic: -3.076872548034501\n",
      "p-value: 0.028312743499213165\n",
      "Critical Values:\n",
      "   1%: -3.6327426647230316\n",
      "   5%: -2.9485102040816327\n",
      "   10%: -2.6130173469387756\n",
      "Reject null hypothesis: Data is stationary\n",
      "\n",
      "\n",
      "KPSS Test for Dependent Variable Entropy Publishers:\n",
      "KPSS Statistic: 0.1573529637132756\n",
      "p-value: 0.1\n",
      "Critical Values:\n",
      "   10%: 0.347\n",
      "   5%: 0.463\n",
      "   2.5%: 0.574\n",
      "   1%: 0.739\n",
      "Fail to reject null hypothesis: Data is stationary\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_715308/1732045873.py:17: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
      "look-up table. The actual p-value is greater than the p-value returned.\n",
      "\n",
      "  result = kpss(data)\n"
     ]
    }
   ],
   "source": [
    "# Function to conduct the Augmented Dickey-Fuller (ADF) test\n",
    "def adf_test(data, variable_name):\n",
    "    result = adfuller(data)\n",
    "    print(f\"Augmented Dickey-Fuller Test for {variable_name}:\")\n",
    "    print(\"ADF Statistic:\", result[0])\n",
    "    print(\"p-value:\", result[1])\n",
    "    print(\"Critical Values:\")\n",
    "    for key, value in result[4].items():\n",
    "        print(f\"   {key}: {value}\")\n",
    "    if result[1] < 0.05:\n",
    "        print(\"Reject null hypothesis: Data is stationary\")\n",
    "    else:\n",
    "        print(\"Fail to reject null hypothesis: Data is non-stationary\")\n",
    "\n",
    "# Function to conduct the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test\n",
    "def kpss_test(data, variable_name):\n",
    "    result = kpss(data)\n",
    "    print(f\"KPSS Test for {variable_name}:\")\n",
    "    print(\"KPSS Statistic:\", result[0])\n",
    "    print(\"p-value:\", result[1])\n",
    "    print(\"Critical Values:\")\n",
    "    for key, value in result[3].items():\n",
    "        print(f\"   {key}: {value}\")\n",
    "    if result[1] > 0.05:\n",
    "        print(\"Fail to reject null hypothesis: Data is stationary\")\n",
    "    else:\n",
    "        print(\"Reject null hypothesis: Data is non-stationary\")\n",
    "\n",
    "# Apply the ADF and KPSS tests to the data\n",
    "non_zero_entropy_locations = shannon_diversity_locations[~((shannon_diversity_locations == 0) | \n",
    "                                                               (shannon_diversity_locations.isna()) | \n",
    "                                                               (shannon_diversity_locations == np.inf) |\n",
    "                                                               (shannon_diversity_locations == -np.inf))]\n",
    "\n",
    "adf_test(non_zero_entropy_locations, \"Dependent Variable Entropy Publishers\")\n",
    "print(\"\\n\")\n",
    "kpss_test(non_zero_entropy_locations, \"Dependent Variable Entropy Publishers\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "91"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " (len(df_location_publishers))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_location_publishers_renamed = df_location_publishers.rename(index= {\"universidad de antioquia\":\"UdeA\",\"universidad nacional\": \"UNAL\", \"universidad de los andes\": \"Uniandes\",\n",
    "                             'tercer mundo': 'TercerMundoEd', \"editorial norma\": \"Norma\", \"banco de la república\": \"BRC\",\n",
    "                           \"instituto caro y cuervo\": \"CaroyCuervo\", \"el áncora editores\": \"Áncora\", \"fondo universidad eafit\":\"UEafit\",\n",
    "                           \"seix barral\": \"SeixBarral\", \"plaza y janés\": \"PlazayJanés\", \"pontificia universidad javeriana\": \"UJaveriana\",\n",
    "                          \"ministerio de cultura\": \"MinCultura\", \"villegas editores\": \"VillegasEd\",\"universidad del rosario\": \"URosario\",\n",
    "                          \"universidad externado de colombia\": \"UExternado\", \"universidad del valle\": \"UValle\", \"carlos valencia editores\":\n",
    "                          \"CarlosValenciaEd\", \"alcaldía mayor de bogotá\": \"AlcaldíaBogotá\", \"arango editores\": \"ArangoEd\", \"fundación simón y lola guberek\": \"FundaciónSyLGuberek\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_treemap_labels(row, total_size):\n",
    "    if row['num_books_per_location'] >= 25:\n",
    "        return f\"{row['location_publisher']}\\n{row['num_books_per_location'] / total_size * 100:.2f}%\"\n",
    "    else:\n",
    "        return \"\"\n",
    "\n",
    "# Calculate the total size\n",
    "total_size = df_location_publishers['num_books_per_location'].sum()\n",
    "\n",
    "# Apply the function to create the treemap_labels column\n",
    "df_location_publishers['treemap_labels'] = df_location_publishers.apply(create_treemap_labels, axis=1, total_size=total_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = df_location_publishers[\"treemap_labels\"]\n",
    "sizes = df_location_publishers['num_books_per_location']\n",
    "# Plot the treemap\n",
    "plt.figure(figsize=(5, 6))\n",
    "squarify.plot(sizes=sizes, label=labels, color=sb.color_palette(\"Spectral\", len(df_location_publishers)), alpha=0.75)\n",
    "plt.axis('off')  # Remove axis\n",
    "plt.title('Treemap of Locations Counts')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
